{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to `darts`\n",
    "In this notebook, we will go over the main functionalities of the library: how to build and manipulate time series, train forecasting models, make predictions, evaluate metrics, backtest models and ensemble several models.\n",
    "\n",
    "As a toy example, we will use the well known [monthly airline passengers dataset](https://github.com/jbrownlee/Datasets/blob/master/monthly-airline-passengers.csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from functools import reduce\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.models import (\n",
    "    NaiveSeasonal,\n",
    "    NaiveDrift,\n",
    "    Prophet,\n",
    "    ExponentialSmoothing,\n",
    "    ARIMA,\n",
    "    AutoARIMA,\n",
    "    RegressionEnsembleModel,\n",
    "    RegressionModel,\n",
    "    Theta,\n",
    "    FFT\n",
    ")\n",
    "\n",
    "from darts.metrics import mape, mase\n",
    "from darts.utils.statistics import check_seasonality, plot_acf, plot_residuals_analysis\n",
    "from darts.datasets import AirPassengersDataset\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data and build a `TimeSeries`\n",
    "A `TimeSeries` represents a univariate or multivariate time series, with a proper time index. The time index can either be of type `pandas.DatetimeIndex` (containing datetimes), or of type `pandas.Int64Index` (containing integers; useful for representing sequential data without specific timestamps). In some cases, `TimeSeries` can even represent *probabilistic* series, in order for instance to obtain confidence intervals.\n",
    "\n",
    "`TimeSeries` can be built easily using a few factory methods:\n",
    "\n",
    "* From an entire Pandas `DataFrame`, using `TimeSeries.from_dataframe()`.\n",
    "* From a time index and an array of corresponding values, using `TimeSeries.from_times_and_values()`.\n",
    "* From a 1-D or 2-D Numpy array, using `TimeSeries.from_values()`.\n",
    "* From a Pandas `Series`, using `TimeSeries.from_series()`.\n",
    "* From an `xarray.DataArray`, using `TimeSeries.from_xarray()`. \n",
    "* From a csv file, using `TimeSeries.from_csv()`.\n",
    "\n",
    "Here, we directly load the air passanger series from a Darts dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9WULXm3ILSEAXQpwl3DP0U6dOAY0nQ/fmgihIyUUIcZZwD+jW7j7+zNCzs7MBaNOmTbnH/THL5b///S8Affv2rfE494Bu/RUSaBLQhRDllJSUsHDhQn75y1/Ss2dP1qxZ49f2i4qKOH78OKGhobRp0yYgAd1a5dBa9dDia4a+e/duXn/9dUJDQ7n//vtrPDY+Pp5mzZqRn5/vt12SPJGALoQo59577+W6665j8eLF7N69myVLlvi1fatk0a5dO0JCQvx+UbSkpISsrCwcDgft27cv95yvr/XUU09RVlbGzTffbK8RUx2Hw1HvZRcJ6EKIcqyMfOjQocCZi4D+4l5uAfyeoR86dAjDMGjXrl2lzSeskktOTk6t23XPzv/whz94dY4EdCFEg9qzZw8At9xyCxD4gO7vDL26cgucuZBZlzEtXbqUsrIyJk6c6DE7t9T3RhcS0IUQtpMnT3L8+HGioqJITTX3rgl0QI+NjcXhcHD69GlKS0t9br+mgG69pjVtsjZ+/vlnAIYMGeL1OdZFWWv/0UCTgC6EsO3duxcw1/m2gp+/A7r7HHQwa81W2SUvL6/a87xVU0Bv164dYI7J6XRWer4mu3btAqBHjx5enyMBXQjRYKxyS/fu3cuVJ/w57a5ihg7+LbvUFNAjIyNJSEigtLS01kF29+7dAPTs2dPrc1q3NnfitJYKCDQJ6EIIm3uGHhMTQ7NmzSgqKvLrlMKqAro/L4zWFNDdX9fqhzfKysrsNzvJ0IUQjYJ7hg6+XUSsjvvSuRYrQw/WgJ6RkUFJSQnt2rUjJibG6/MkQxdCNJj6DOhVZeiBLrm4v25tAnpdyi0gGboQogFZJZeKAd26GchXpaWlZGdn43A4yq2F4q+SS1lZmX3R1f0vAHfW47WZ6WJdEK1tQLcy9KNHj9bL7f8S0IUQABiGYWfo3bp1A/yfoWdnZ2MYBm3atCEs7MzagP66KJqdnU1paSlt2rQhKiqqymN8ydBrUz8Hc0pmdHQ0hYWF9iJkgSQBXQgBmEvO5uXlERcXZy876++AXlW5BfyXoXsqt7i/dm0Cel0zdCifpQeaBHQhBFC+3OJwOAD/B3SrHWs+uKUpB3Srjn7kyBE2btzI5MmTefXVV2vdjjckoAshACqVW8D/Ad2a7VFxWVt/lVwCFdDrWnKB8hn6xo0b+fe//83XX39d63a8IQFdCAFUnuEC5e+s9Aer7GAFOUtDZOgHDx706kJlTk4OOTk5xMbG1riHaHXcM3TrjcH9e+xPEtCFEEDlGS7g/wy9uoBenxl6bGwscXFxFBcXe7Xqonu5xSpF1YZ7hl6Xm5NqQwK6EAKoOkP39+3/VkCvWHKpzwwdzkxd9Kbs4ku5BSRDF0I0ACsYdu7c2X6sWbNmxMTEUFBQQH5+vs+vEeiSy4EDBwDPAb02dXRfLohC+Qzd1zcHTySgCyGAM/twVtz82J9lF+uiaCBKLjk5OezZs4eIiAi6du1a47E1BfQXX3yRSZMm2Ss/7tixA8DrNdArsjL0jIwMDh48SGhoqMc3nLqSgC5EI7Jq1aqAzGd2Op3VZs/+DOiBzNBXrVoFgFKKyMjIGo+tLqAfOnSImTNn8t133/HZZ58BsGHDBgD69+9fp35ZY9VaA9C1a9dyN1X5kwR0IRqJbdu2MXToUM4//3y/rBvuLicnh7KyMlq2bElERES55/x5+38gL4qmp6cDMGzYMI/HVrfRxfPPP09RURFgvkEUFRXx448/4nA47A0/aqviei6Bqp+DBHQhGo1169YBZk33N7/5jV/btsotFS9Wgv8y9LKyMo4fP47D4bDvRLVERUURFhZGUVGRHVBrywrow4cP93hsVRl6Tk4O//jHP+yvV61axdatWyktLaVXr17ExsbWqV8V37wCVT+HWgR0pdQkpdQR178nKqVWKqW+Ukp1cj3WRym13PX4mEB1WIizlbUFGsD8+fN5//33/da2FdCrmmftr4Cek5ODYRi0bNmyUsmhLrsWGYbBI488wuLFiykpKbE3t65Nhu4e0OfMmUN+fr69xdwPP/zA6tWrARg4cKBXfapKQkJCuemODZ6hK6VCgYnAAaVUGHA/cCEwC3jEddhs4DbgMuAJv/dUiLOcFdCHDh0K4NcsvaaA7q+bi6ort1hqW3bZvHkzTz75JJMmTeLDDz/k9OnTnHPOOV7d/GPN5Nm/f7/92MsvvwzA7NmzSUpKoqioiAULFgAwYMAAr/pUldDQ0HJ/kQRDhj4JWAg4gXOAbVrrYq11OmAVljporXdqrU8Cx5VSVf/UhBB1YgX0p59+mqioKDIyMvwylRDOzD6pKUP3tYZe3QwXi/W4t28cVn8KCwuZNm0a4F12DtClSxccDgcZGRmUlpaSm5vLwYMHiY6OZvTo0QwaNAjALxk6lC9lNWiG7srOrwPecT3UEnC/FB1aRVu5QPkimRDCJ1ZAP+ecc+xpb9bccV/VVEO3yhO+BnRPGbo11XDfvn21ag/OZPXe1M/B3Fu0Q4cOlJWVkZGRUW7rPYfDYQd0iy8ZOpQfcyAzdG/mzkwG3tVaO5VSACeA5m7Pl7k+u2+h3QI4XrEhpdQdwB0AM2bM4JJLLqlDl+tPSUkJmZmZDd0Nv5CxBCdvx5Kbm8vRo0eJiorC6XTSpk0bfv75Z9avX09cXJzP/bDuEo2IiKjUH6v+m5GRUWNfPY1l586dAMTExFR5XKtWrQDYtGkTF1xwgcc+W+1FRUVRWFgImHPFvf3d6NChA5mZmaxdu9aeLtm+fXsyMzNJSUmxj0tMTPT5d65Zs2aAuexAYWGhT2117Nix2ue8CejJwECl1GTMcsv/AecqpSIABWxyHZellOoJZAMJWutKk2W11nOBua4vA799h48yMzNr/OY1JjKW4OTtWKzs2MrOk5KS+P777ykoKPDL9+L06dMA9OrVq1J7Vv33yJEjdOjQodr1TDyNpazMzP26du1a5XF9+/YFzIun3oyptLQUMJPDJUuWEBERwahRowgJ8a6S3KtXL9auXUt+fr59IbZPnz507NiR0tJSYmNjOXXqFGlpaT5/j62afc+ePQN2UxF4EdC11jOtfyultNb6V0qp64FvgEJgiuvpPwALMEswj/q9p0Kcxaxyi3W3ohUgrFvdfVVTySU6OpoWLVqQm5tLTk5OpSmH3vJUcrGW7fW25GLV5Lt3787mzZsJDQ31Ophb54G5KNmJEyfKPRYWFsbgwYP55ptvfK6fw5nvayDr5+Bdhm7TWivX53c4U1O3ntsKjPRf14QQlvoK6NXNEGnfvj25ublkZWXVOaB7uiha2xq6+9rqnu4MrYr1BrJ371571UX3gHvrrbeyZcsWrr322lq3XZHVbr9+/XxuqyaBuf9UCOFXgQ7oNc1yAXPq4vbt28nKyrJLI7Xl7UXRvXv3YhiGx6VqPbXniRXQ9+zZUylDB7jlllu45ZZb6tR2RTfffDMJCQlcfPHFfmmvOhLQhWgErAuAgQjopaWlHDt2jJCQkGqz77rs8lNRdUvnWlq0aGGXdo4ePVrtcZbqdj/yVlUB3X23Jn+KjIxkwoQJAWnbndz6L0QjUFOG7us65VagbdWqFaGhoVUe44+pi95k1LWpo/uaoXfu3JmQkBB7Pn/z5s1p2bJlndoKFhLQhQhyeXl5HD58mMjISHuGRHx8PLGxseTn5/u8y4+n+jmcuVvUHxl6TQHYvexSE6fTybFjx4Az0x1rKyIiotzsFffNsRsrCehCBDlrg4UePXrYszgcDoedpft6c5Gn+jn4XnIpKioiLy+PsLAwe82Wqnh7YTQnJwen00l8fDzh4eF16hOUL7EEegZKfZCALkSQs3a5qbhjjr/q6DVNWbT4WnJxz85ryoK9Lbn4Wj+3uAfxQNXP65MEdCGCnHVXofvWcHBmmzV/BfRAllw8XRC1eFty8bV+bpEMXQhRr6xNGKyNjS3+ytDro+TibQD2tuTirwxdAroQopIvv/ySp59+GqfT6fngWgp0QPcmQ7d2Mjp58qS9TEBt+DugByJDl5KLEAKAe++9l4cffphPP/3U723XV0CvKdt1OBx22cWbOvp//vMfRo0aZffdWnfcUwBu3bo1MTEx5Obm2nPD3RUWFuJ0OgNSQ5cMXQiBYRh2zXfhwoV+bz8YMnSoXdnln//8J8uXL+eFF14A4L333gNg5MiaVwdxOBzVZulbt24lPj6ehx56yOMyAt7q3LkzvXr14vzzz7dXRGzMJKAL4aOcnBy7DLF48eI674lZHW8Curc3FxmGwV/+8hd7/03wroYOtQvoVkY+f/58Nm3axJo1a2jevDlXX321x3OrC+jvv/8+RUVFvPrqq3YffM3QQ0ND2bJlCytWrPCpnWAhAV0IH7lnyLm5uXz55Zd+a/v06dOcOHGC8PDwSjfQxMXF0aJFCwoLC+2bbDxZtWoVM2fOZOzYsezZs4eMjAx7HrungO5tycXaNALg2LFj3HjjjQBMnDiR6Ohoj33s0qULUPkvj2+//RaAEydO2KUtXzN0gPDw8GrvkG1sJKAL4aOKgefdd9/1W9tWJlrdOuS1vbnI6mteXh6TJ0/m2muvpbCwkMsuu4z4+Pgaz/U2Qz906JC9VjnAjz/+CJgLVHnDCuju+30WFxezcuVK+2trQwpfM/SmRgK6ED6ygumIESMAWLJkid/KLu4BvSq1raO7Z9crV65k9erVdOnShTfeeMPjud4GdCsQ9+7d265Ld+3a1WP93FLVBs5r166loKCgUp3bHxl6UyIBXQgfWcF0zJgxpKamkpuby9dff+2Xtqurn1vqGtDHjBmDw+EgMjKS999/36tM19ubi6y+JCcn28vPTpkyxevNJ6oquVjllsmTJ5fb8Ucy9PIkoAvhIyvwdO7cmdGjRwOwYcMGv7TtKaDX9m5RK6Bff/31pKens3btWlx7BXtkBVpPd3FamXWXLl34y1/+wptvvsnDDz/s1Wu4v457hm4F9AsvvNC+sBoREdEkZqb4kwR0IXxklVw6d+5McnIyYE6x84dAZejt2rVj6NCh5TZD9sTarX7Pnj2VbqBauXIlH3zwAVA+oMfGxjJ58uRa7ShkrYCYmZlJaWkpJSUl9qycUaNG8ctf/hIwN29u7Ksj+ptscCGEj9wz9JiYGAC2bdvml7YDGdBrKy4ujrZt25Kdnc3Bgwfp1KkTRUVF/OEPf+C5554DYNiwYeUCel1ERkbSrl07Dh06RFZWFgcPHuTUqVP07t2bdu3a0bZtWx5++OGAb+fWGElAF8IHhmHYGXqnTp3sqX/btm3D6XTWatPiqgRTQAczS8/OzmbXrl107NiRcePG8dVXX9nPf/nllz4HdOvcQ4cOsX//fnt2y6hRowAICQnhqaeeqnPbTZmUXITwwdGjRyksLKRFixbExcXRqlUrEhMTOXXqlF+2h/O2hp6RkeFxHRmn0+n1XaHVsZbw3bVrF4cPH+arr74iNjaWBx98EIBly5b5JaC7z3RZu3YtAEOHDq1ze2cLCehC+MC9fm4599xzAf+UXTwF9JiYGFq1akVJSYkdrKtz7NgxysrKSEhIqFVN250V0Hfv3s2mTZsAGDRoEA888AAAy5cv59ixY0RERNT5TQPKz3TRWgN4ffH2bCYBXQgfuNfPLf66MJqXl0deXh7R0dG0aNGi2uO8LbtY5ZbExMQ698m6MLpr1y42b94MQEpKComJifTu3ZvCwkK7T76Um6yAvn79evbs2UNMTAx9+vSpc3tnCwnoQvjACqLuc6P9laF7ukvU4u3dor7Wz6F8ycU9oIN5QbRin+rKCugff/wxAAMHDiQsTC75eSIBXQgfBDJD91RusdQ2Q/dHQHcvuaSmpgIwfPhw+zhf6udwZkx5eXmAlFu8JW95Qvigqhq6e0A3DKPOc6WDMaC3a9eO6Ohojh07Rm5uLoA9fXDo0KGEhITgdDp9DugVz5eA7h3J0IXwQVUll8TEROLj4zlx4gSHDx/2uq2PPvqI2267jb1792IYBl988QVwZg2V6nh7t6g/ArrD4bDr6KWlpXTt2pXmzZsD0KJFCwYNGgT4nqG3adOm3IVbCejekYAuhA+qKrk4HI46lV0ef/xx5s+fT2pqKpdffjkLFiwgLCyMCRMm1HheTRn6+vXrOeecc3jttdf8EtDhTNkFzpRbLL///e8ZPnw4V1xxhU+vERISYr9RxcXF0atXL5/aO1tIQBeijtzX/a54EdAK6LW5MLpr1y7ArBt/9tlnxMTE8OGHH3pcpbC6gF5SUsKtt97Kzz//zKxZs+znfQ3oVoYOVFo6YMKECaxYscLn14AzWX5aWprPN2idLeS7JEQdHTx4kJKSEhITE+1b/i3WFLvt27d71daJEyfIyckhJiaGefPmMXbsWJYtW8Zll13m8Vxr7ZODBw9SVlZmP/7888+zceNGwJwxY+3K488MvTZrwdSWFdCl3OI9jxdFlVKJwCKgBCgDbgJ6An8BnMCvtNablVLtgDeAWOBlrfVbAeu1EEHAWnWwqt3izznnHAB27tzpVVt79uwBzOx32rRpTJs2zet+REZGkpiYyOHDh8nKyqJTp07s3r2bxx57DIArr7ySpUuX2tvU+TIP3eqjpWLJxZ9uuukm1q5dy5QpUwL2Gk2NNxn6UWCE1noUZsC+DXgKGAfcCPzZddxMzCA/CrhHKRXl/+4KETz8GdB3794NlA+WtVFxadtXX32VgoICbrjhBv71r38RFWX+dwwJCfF5UwgrQ4+IiLDHGQiXXHIJP/74oyzCVQseA7rWukxrbS0SEQfsAsq01jla6/1Aguu5IcAyrXUpoAH5KYgG9/PPP9OxY0eef/55v7dtZdXdu3ev9FyPHj0ICQlh7969FBcXe2zL14Deu3dv4EyJx9r27ZprriEhIYEbbrgBMNdw8XX/zHPOOYfp06fz6KOPEh4e7lNbwr+8qqErpQYopVYDM4CVwEm3p0uVUhFAuFvgz+VMoBeiwXzyySccPHiQ3/3ud2zZssWvbdeUoUdGRtKlSxecTqcd+Gvia0CvOKvGCuxWLf/uu+/G4XDYd7H6IiQkhFdffbVWm1aI+uHVjUVa6w3AeUqp64A/AM3d29BaFyulSpRSIa6g3gI4XrEdpdQdwB0AM2bM4JJLLvG1/wFVUlJCZmZmQ3fDL87Wsaxfvx4w50xPmTKFxYsX+23GxI4dOwBo1qxZlf3p0qULe/fuZdWqVdXurGONxZoN06JFizr9nKyFsNavX8/u3bvZvXs3ISEhxMTEkJmZSYcOHVi6dCnt2rUL2O/B2fo7Vt+si+BV8eaiaITW2vqbMRfIB8KUUvGYJRgrcK8FLlRKLQfSgN9VbEtrPReY6/rS8LL/DSYzM7PGb15jcraOxVoPxeFwsG7dOj788EPuvvtuv/TDalspVWV/+vXrx/Llyzl+/Hi1/bXGYgWPwYMH1+nnZE1t3L17N4WFhZSVlZGUlFQu4w/0z/9s/R0LJt6kKgOUUsuVUl8DvwGeAf4IfAK8DfzeddyfXf9eDryitS7wf3eFqB3rouTjjz8OwEsvveSXdktLS+11v7t27VrlMd5eGC0rK6uxfOONHj16EBERUW79cFmd8OzjMUPXWq8BLqjwcBYwrMJxWUBw11DEWaW4uJi9e/cSEhLCvffey2OPPcZPP/1EYWGhPeujrjIzMykrK6N9+/bVtuVtQM/IyKC0tJQOHToQHR1dp/6EhYXRq1cvtmzZwuLFiwEJ6GcjubFINFnWZsZdunShefPmJCUl4XQ6+emnn3xu25uM2rpdvbqAXlZWRlFRkc8XRC3WhdHPPvsMkIB+NpKALposK5BamXLfvn2BM1P6fFHTlEVLt27dCA0NZf/+/fbGD5aMjAySk5MZMWIEy5YtA3wP6NYMFuu1JKCffSSgiyarYkC3Mlh/BHRvMvTw8HC6d++OYRh2Fg6QnZ3NxRdfzE8//URWVhZPPvkk4L8M3SIB/ewjAV00WYHM0L29iFmxjl5WVsbll1/Ojh07SElJKXe+vzJ0gNatW9OqVSuf2hONjwR00WRZtfKGKrm4v7bVl61bt/LDDz/Qtm1bvvjiC+bPn09cXBxw5m7PuurVq5c9x94fNxCJxkcCumhw27Zt48UXXyy3UqA/VMzQe/fuTWhoKLt27apU066tumboVuklLS2NxMREevXqxTfffMMrr7zC4MGDfepTZGSkvc6KlFvOTrIFnWhQx44dY8yYMWRlZdGjRw+fN0awFBYWcuDAAUJDQ+0sOjIykqSkJHbs2MGOHTvo379/rdrMz8/n448/Zs2aNWRkZOBwODxuhmzNdLHuKq1qRsugQYPsnX58lZyczM6dO33O9kXjJAFdNBjDMLj99tvtOy43bdrkt4C+a9cuDMOge/fu5RaQSk5OZseOHfz4449eB/Tc3FxmzJjB+++/T0HBmfvlBgwYUG6btKpYmbIV0K1NLHytl1fnvvvuo6SkhMmTJwekfRHcJKCLBjNv3jwWLVpkf12b7do8qVhusfTt25dFixbVqo7+wQcf8NZb5vL+w4cP57LLLqN///6MGjXK47mdOnUiOjqaw4cPc+LECTtDd98kwp9GjRrlVb9E0yQ1dNEgDMNg1qxZAPbaKrXZrs0TKyOuKqBD7S6MWv2aNWsWK1as4I9//CNXXHGFvTlyTUJCQsqVXfx1E5EQVZGALhrEjz/+SFZWFu3bt7fnYW/fvh2n0+nhTO9s2LABoFJZxQrotflrwJqhUnGet7esssu2bdu8nh0jRF1IQBcN4quvvgJg9OjRtGzZkvbt21NQUMC+ffv80r61bO6AAQPKPd6rVy97psvp06e9asvK9ut6odE6b9myZRQXF5OYmFjtcrpC+EICumgQVkAfM2YMcGbetD/q6KdOneKnn34iLCzMzsgtkZGR9OnTB6fT6VXZpbS01L6QWdft1qyAbq2xIuUWESgS0EW9Ky0t5dtvvwXOBHSrnOGPOvrmzZsxDIPk5OQqZ6FYZZiNGzd6bGvv3r2UlJTQqVMnYmNj69QfK6AfOXIEkIAuAkcCuqh3WmtOnjxJUlKSvbmxPzN0q9wycODAKp+vTUC36ue+zOuueG6gZrgIIQFd1LuK5Rbwb4ZuXRCtWD+31Cag+1o/B3OLOvfdbyRDF4EiAV3UqLS01O9tVhXQ3TN0w/Btd8LqLoharIC+adOmal+rqKgIOBPQramHdeX+hiABXQSKBHRRrZ07d9K2bVtmzJjhtzaPHj1Keno6DoeDiy66yH68bdu2JCQkcPLkSQ4ePFjn9ktLS9m8eTNQfUBv164dbdu2JTc3t8pZNQsXLiQ6Opo5c+b4peQC5ddWkZKLCBQJ6KJaTz/9NDk5OSxZssRvbS5YsIDi4mLGjh1L69at7ccdDoedpftSdtmxYweFhYV069aN+Pj4ao9LTU0FKpddDMPgsccewzAMHnzwQTvb9zWgW+dHRUXRrl07n9oSojoS0EWVMjIy7NvdMzIyOHHiRK3OP3DgAJdeeinXXnstTzzxhL0d3Ny5cwG46667Kp1Tlw0otm/fzjfffGN/bdXPq7sgaqmujv7ll1/aF2YLCgo4ceIEkZGR9sXburICevfu3e0lboXwN/nNElV64YUXKCkpsb+u7Rri77zzDl988QXvv/8+jz76KOeddx7//Oc/2blzJ506dWLs2LGVzrFKJD/88IPXr3PNNddw0UUXsXTpUgB7O7fqyi2W6gL6Cy+8AMCvf/1rO8NPSkoiNDTU6z5VZdSoUVxzzTU89NBDPrUjRI0Mw2ioj6CXkZHR0F3wm9qM5fjx40azZs0MwBgwYIABGK+88kqtXu/uu+82AGPSpEnGqFGjDMD+ePzxx6s8Z+3atQZg9O7d26uxlJaWGmFhYQZgtG7d2pgzZ44BGKGhoca6detqbGPjxo0GYPTs2dN+bMeOHQZgREVFGUeOHDHmzZtnAMatt95aq7HXxtn6Oxbsgnws1cZVydBFJf/4xz/Iz8/n4osv5qabbgJgy5YttWrDWrPk+uuv5+OPP+a8884DIDQ0lNtuu63Kc1JTU4mMjGTHjh1elXiys7PtWThHjx7lnnvuAeCZZ57xWHLp06cP4eHh7Nq1i7y8PABeeeUVAG6++WZat27NtGnTWLt2Lc8//7znAQsRBCSgi3IKCgp48cUXAZg5cyb9+vUD6h7Qu3fvTmxsLB9//DHjxo1j1qxZ5eZku4uIiLADsdba42tkZmYC0LVrV3v/zOuvv57f/OY3Hs+NiIiwL4xar2XV4q03MQClFC1atPDYnhDBQAK6KOdf//oXR44cIS0tjTFjxtgB3bqd3huGYVTaoq1Vq1Z89NFH9pK51RkyZAgAa9as8fg6GRkZAKSkpPDFF1/w1FNPMW/ePBwOh1f9PP/88wFYvXo1BQUFbNq0iZCQEJRSXp0vRLCRgC5spaWlPPvss4CZnTscDjp27EiLFi04duwY2dnZXrVz6NAhCgsLSUhI8GrNcHd1CeidOnVi4MCBPPzww7Vab8UK6KtWrWL9+vWUlZXRr1+/Oq/ZIkRDk4AubIsWLWLPnj0kJSUxYcIEwJwf7p6le8OXNb+tgL569WqPfxG4B/S6cA/oq1evLvf6QjRGEtCF7euvvwZg+vTp5abp1baObpVb6hLQk5KSiI+P59ChQ3bAro6vAb1nz560atWKw4cPs3DhQkACumjcJKALm3WHZsVdfmob0H3J0B0Oh9dlF18DusPhsLP077//HpCALho3CejCZt0had2Cb6nPgA7YUxwDHdDhTNkFICYmptKGGEI0JhLQBYB90TM2NpbOnTuXe84K6Js2baKwsNBjW74GdGvqYk3L2xqGYQf06qZBesM9oKelpREWFlbntoRoaB5/e5VSQ4AXgRIgE7gFuBq4DygApmitM5RSfYC5rjYf0Vp/FahOC/+zyi3nnntupbVGWrduzcCBA1m/fj3Lli3j8ssvr7EtXwN6dbflnzp1igULFhAWFsY111xDUVER8fHxPu3POXjwYBwOB4ZhSLlFNHreZOgHgNFa6wuAvcBVwP3AhcAs4BHXcbOB24DLgCf83VERWNWVWyxXXHEFAB9++GGN7ZSWlrJ//37AvOGnLrp160ZcXByHDh2yp0q+/vrrJCUlMWPGDH71q1/Zs1J8KbcAtGjRwl4UbPDgwT61JURD8xjQtdZZWusC15fFQG9gm9a6WGudDqS6nuugtd6ptT4JHFdKta6qPeGbF198kauuuoqrrrqK+++/328bUFgZuhXcKnIP6DVNJ8zIyKCsrIwOHToQFRVVp76EhISUW95206ZNTJ06lUOHDhEZGYlhGPZ8eV8DOsDs2bOZOnUqV155pc9tCdGQvC4YKqW6ApcCDwFt3J6y5re5vznkAgnA0Qpt3AHcATBjxgwuueSSOnS5/pSUlNi3lweDo0ePVrqtPTk5ucqVCyvyNBZr3e+2bdtWeVxiYiKJiYlkZmby+eefk5KSYj9XWlrKypUrSU9Pt8/t2LGjT9+7pKQk0tPTWb58uf0GctVVVzF27Fjuuusu+zb9li1b+vwzSktLIy0tjePHj/vUTl0E2++YL2Qs9aOma0ZeBXSlVHPgTWAqZgB3v/2vzPXZ6fZYC6DS/w6t9VzMOjuYK+8FtczMTJ8uuPnbd999B5hT6wYPHsycOXNYunQp06dP93iu+1i2bdtGfn5+uRLD7t27ARg5cmS1Y77qqquYO3cuq1ev5rLLLgPggw8+4O677+bw4cPlju3fv79P37thw4bx+uuvs3fvXo4cOQKY67T88pe/5He/+x0nT54EzK3hgulnVFvB9jvmCxlLw/NYclFKhQFvA49rrXcAO4FzlVIRSqlhwCbXoVlKqZ5KqTggQWt9tJomRR1Ze3FOmDCBWbNmERYWxieffMKhQ4e8buP06dOMHDmSYcOGsXPnTgDy8vI4cOAAERERNV7ItEoS7nX0p556isOHD5OUlMRDDz3EM888w4svvsiTTz5ZlyHarJLL2rVrWb58OQAXXXQRUVFR5f4i8UfJRYimwpsMfRJwHvCIUuoR4GXgBeAboBCY4jruD8ACzAz+UT/3U1B+c+W2bdty+eWXs3TpUt566y1++9vfetXG22+/zbFjxwBzmdm5c+eyfft2wNxVp6Zpe6NHjyY6OpoffviBzMxMYmNjWb9+PREREWzcuJGYmBgfR3hGSkoKDofD3qS5b9++9tZtEyZM4J133gEkoAtRTk2LpQf4I+gF0yL3u3fvNgAjPj7eKC0tNQzDMBYtWmQARnJysuF0Oms83xqLUsreaCIiIsLIzMw0FixYYADGdddd57EfV155pb3hxdKlSw3AGDlypO8DrMI555xj9/XXv/61/fi+ffuMzp07G4Cxc+fOgLx2fQmm3zFfyVjqjWxw0dhZ2flFF11kr7Mybtw42rRpw9atW71aP1xrjdaali1bcsUVV1BcXMzs2bPttqub4eLOvexiXZgcNWpUXYbkkfsSBGPGjLH/HRoayscff8yiRYtISkoKyGsL0RhJQG8k3MstlvDwcCZOnAjAZ5995rENa0eeqVOn8vjjjwMwZ84c3nzzTcDzPpxgvomAuZnyp59+CsCFF17o3SBqyQroISEhld40UlJSuPrqqwPyukI0VhLQg8i+ffuYMWMGWVlZ5R4vLS21Nz92D+hg1rXhzG471dm9ezdvvfUWAHfeeScDBw7kmmuuAcy1U1555RXGjx/vsY/t2rVjyJAhFBUVsW3bNsLDwxk6dKhX46sta6OJ888/X3YNEsILsnBFEHn22WeZM2cOBQUFzJs3DwCn08mtt95KdnY23bt3p3fv3uXOueCCCwBYuXIlRUVFREZGVmrXMAx+//vfU1RUxC233GK38Z///Ifc3FzatGlT6ZyaXHnllfbCWeedd55fL4a6+8UvfsHLL78csJKOEE2NZOhBxLq559133+XUqVMYhsE999zDW2+9RWxsLP/5z38qba/Wpk0b+vXrR2FhYbWrE77xxhukp6fTqlUrnnvuOfvxiIiIWgdzOHPXKASu3ALm8rZ33XVXtcsRCCHKkwzdT/bv38/TTz9NQUEBYWFh3HPPPR53nnfndDrZtMmc0p+fn8/7778PmHXvyMhIPvzww3IrA7q78MIL2bJlC9988w0jR44s99yGDRu4//77AfjrX/9K69a+r8iQkpJCt27d2Lt3LxdddJHP7Qkh/KSmKTAB/gh6tZm69Ktf/cqeYgcY/fv39ziV0J01LdH6GDx4sNGmTRsDMObPn1/jue+9954BGKNHjy73+Oeff240a9bMAIyLL764Vv3xJD093Xj++ef92qa3gnxKWa3IWIJTkI+l2rgqAb0GtfmhWnOmn3zySaNdu3YGYHzyySden2/NKT///PON6OhoO7CPHDnSY9DMzs42ACMqKsooLCw0DMMwtmzZYoSFhRmAceONNxq7du3yui/BLsj/s9WKjCU4BflYZB56IO3fv5+dO3fSvHlzZs6caZc4Zs+e7XUb1trfI0eOtGefhIWF8fLLL1eqm1dUVR194cKFlJaWct111/Hmm29WebFUCNG0SED3A2uO+KhRowgLC+Ouu+4iPj6eFStWsGLFCq/asAJ6//79ue+++4iLi+OJJ57weks0q5b98ccfA/C///0PgMmTJ1fasEII0TTJ/3Q/qHjTT1xcHP/3f/8HmOuleMMK6KmpqQwaNIjc3Fx+//vfe90HK6t/++23OX78OKtXryYsLCygs1CEEMFFArqPDMOo8i7Ou+66C4Bly5ZRVlZW5bmWkydPsnv3biIiIujTpw+AxzJLRSNHjqRz587s27ePP/3pTzidToYPH05cXFyt2hFCNF4S0H20bds2Dh06RGJiYrnySIcOHejWrRv5+fn8+OOPNbaxefNmwFxLJTw8vE79CAkJYdKkSQD87W9/A+DSSy+tU1tCiMbprAjoWVlZpKenk56ezoEDB/zatpWdjx49ulJWbd0Sv2rVqhrbWLJkCVB+Maq6uOmmmwBzTjuYd1oKIc4eTT6g5+Tk0LdvX0aMGMGIESPo06cP+/bt81v71hoqFddYAewbgaoL6E6nk/vuu8+us0+YMMGnvqSmptKvXz8AWrVqVasbm4QQjV+TD+ivv/46OTk5tGvXji5dunD69Olyt7/7wjAMe1s4a00Vd1ZA//777ys9V1hYyPXXX88LL7xAeHg4b731ll82KZ48eTIAl112mcxuEeJsU9Mk9QB/BFxZWZl9w88HH3xgbNq0yb4B5/Dhwx7Pd7+54L///a+xePHics9v377dAIzExMQqb/4pKioyIiMjDcA4fvy4/fjRo0eN4cOHG4DRvHlz46uvvvJhlJVf86WXXjIOHjxY7VgaOxlLcJKx1Juz88aiZcuWsXPnTjp27MgVV1xBSkoK48ePp7CwkBdffNHrdnbt2sWkSZO4+uqrmT9/vv24Ncd8xIgRVc5KiYiIIC0tDaDcwlm33HIL6enpdOrUifT0dHsJXH+IiIhgxowZtG/f3m9tCiEahyYd0P/xj38A5vrf1l6ZDz/8MGBu7JCbm+tVO9b+lQDTp0+3v7bKLRUXxHJXsexSXFxsX0hdsWKFXfMWQghfNYmAXlhYWOmxjIwMlixZQlhYGNOnT7cfHzp0KCNHjiQ3N5f33nvPq/atAD527FgMw+Dmm29mx44dtQro1oXRTZs2UVRURO/evenatat3AxRCCC80+oD+97//nejoaK6++upy873nzp2L0+lkwoQJlcoPN998M4C9RG1Ntm7dyqZNm4iPj2fx4sVMmTKFkpISbr/9dnbv3k2zZs1ITU2t9nxr6uLKlSspKSmxSy9Dhgyp9ViFEKImjTqgO51Oe8bKkiVLSElJ4Z///CfFxcW8+uqrANx9992VzrvqqqsICQnhyy+/5MSJEzW+hpWdT5gwgYiICGbPnk1MTIydnQ8bNswu51SlU6dOJCcnk5eXx3fffScBXQgRMI06oK9YsYK9e/fSqVMn7r77bgzD4N5772X27NkcOnSI5OTkKqcTtm3blpEjR1JSUmIvZlUVwzB4++23AbjhhhsA8w7QBx54wD6mpnKLxdrh56OPPpKALoQImEYd0N944w3AnDUyZ84cbrvtNoqKiuwd7e++++5q10SxFrOqqezy+eef89NPP9G+fftyO/M8+OCDtG3bFsCr/S6tzZcXLlzI9u3biYiI8PmuUCGEqKSmOY0B/vDJ6dOnjbi4OAMwtm3bZhiGYeTm5hrdunUzACM2NtbIzc2t9vyMjAwDMKKjo438/PxKz5eVlRm9e/c2AOOll16q9Py6deuMV1991asde0pKSoyEhAR704ohQ4bUYqT+EeTzamtFxhKcZCz1punNQ3/nnXfIy8tjyJAh9gqFzZs354033qBZs2bcd999NG/evNrzO3bsyPnnn09BQYFddsnOzub666/nz3/+MwsWLGDHjh106dKF22+/vdL5AwcOZPr06V6tihgWFsbll19ufy3lFiFEIDTKTaJXrFjBPffcA8Btt91W7rmRI0dy4sQJQkNDPbZzww03sGrVKl5//XWuu+46nnvuOd59913effdd+5hZs2b5Zbef8ePH89ZbbwES0IUQgdHoMvR169Yxbtw4Tp8+zdSpU8vNMbd4E8wBbrzxRsLCwvjss8/Yt28fCxYsALAz/h49enDLLbf4pd+/+MUv7NkwEtCFEIHQqDJ0a4/MkydPMnHiRF577TWfFqBq06YNV1xxBYsWLeKmm24iOzub5ORkNm/ezMqVK4mJianz+uQVxcfH8/e//51Dhw7Rq1cvv7QphBDuGlVADwsL49133+X5559n3rx5XmfiNbn11ltZtGgR6enpANx+++2EhIQwYsQIMjMzfW7f3Z133unX9oQQwp3HgK6UagF8ASQD52uttyilJgL3AQXAFK11hlKqDzDX1eYjWuuvAtHhQYMG8eabb/qtvbFjx5KYmMjhw4eJiIiw7yIVQojGxpt6xWlgHPAegFIqDLgfuBCYBTziOm42cBtwGfCEvzsaKGFhYXadfMKECbRq1aqBeySEEHXjMUPXWpcAR5RS1kPnANu01sVAulLqWdfjHbTWOwGUUseVUq211kcD0Wl/e+SRR0hISGDatGkN3RUhhKizutTQWwIn3b62Ctnu2X4ukACUC+hKqTuAOwBmzJjBJZdcUoeXD4ybb76ZkpKScnXzil83ZjKW4CRjCU7BPJaOHTtW+1xdAvoJwP2OnTLXZ6fbYy2A4xVP1FrPxayzg3nXZFDLzMys8ZvXmMhYgpOMJTg11rHUJaDvBM5VSkUACtjkejxLKdUTyAYSGku5RQghmgqvArpS6hNgANAb+CfwAvANUAhMcR32B2ABZgnmUb/2UgghhEdeBXSt9eVVPPxOhWO2Ap7XkhVCCBEQje7WfyGEEFWTgC6EEE2EBHQhhGgiHIYR9LMHhRBCeEEydCGEaCIkoAshRBMhAV0IIZoICehCCNFESEAXQogmQgK6EEI0ERLQhRCiiZCADiilYl2fHQ3dF18ppWJcn5vCWLq6PjeFsZzXFMYBoJTq0tB98BelVMuG7oM/ndU3FimlLgVuBw4Cf9ZaH2zgLtWZUupqYDJwAHimkY8lBvgL0Bm41rVrVqOklOoPvAisAma5dvpqlJRSlwEzgCLgv8BnWuv8hu1V3SilRgEPYG7CMwf4UWtd2LC98t3ZnqHfCLwGbAHuUko1ytUilVLjgVuBP2NuQDLT9XijzAi11qeBYiAOc1yNdiyYK5DO1lo/BPRo6M7UlVIqFLgLc4OaxzH3QohtxD+X64F/Yb4xXQ5c07Dd8Y+6bHDRaLkyv+uBFcBhYD+wBvja9XiaUmpXY8huXWOZBHwKrAOma62PKKV+At5WSrXVWmc3aCe95PZzWa613uUKEj8DHwC/Vkp9prXe36Cd9JL775hrj93TwGVKqYcwN4FZC3yotd7VkP30hmssNwDfAvnAZsy/Zvdh7o8QDYRjvvkGNaVUNOam9p9prb8F9gBZmP//C4FxSqk+WuvtDdhNn501GbpSahLmphwxwG6t9UmgHTDU9WfweiAKc/u8oOY2liggW2t90BXMQzCz2j2NKJhbY4nGfINFa20AyZg/iw+AO5VSnRuqj96qMJa9rodjgPbAb4G7McsV4xqge7VScSxa68PAV5hlvfWYpYrbgXsaqo/ecv3u/Bczifve9bAD6I65FeZWzN+9pAbpoB+dFQFdKdUcuA74E+Yv5cVKqdbAy8B0pVSs1noL0BXo1mAd9UIVY7lQKdUHQGvtxAwgpa5juwTzn8QVxrIMGKWU6ut6+lvMvzxOYQaRX7vOCcrf2SrGcpFSqgPwPmYW21lrnYsZ6K2fT1D+bKr4HRujlDpHa/0N8CUwR2s9GfgIiFBKhQTrWFzCgKWYf4n/n1JqGPA5MAzoq7U+hpkcRUPw/ly80WQvirquxP8W+BhIBy4A7gMigA+BW4BRwB2YP/DvMOu172utP2qIPlfHw1iWYo7lKq31XqXUbZi/qLlAK+CeYLpw5eVYLgXuBC7E3KP2IHBKa/1IA3S5Wl7+jo3BHEcqZiZ4OfCz1vrxBuhytbz8uYzF/OuiPWZAnAHkaK1/3RB9ro7bWJZiXh/r5Po6EzNJmAr8PyAFc8P77cB4zJLfaw3QZb8JymzHV0qpTsBzmLW+dsAbWutPgGeAi7TWzwJvAH/RWv8Z8xf4TmBTEAZzT2N5DvPizp9dp3TBDOg7tdZTgiyYezOWN4DHgGeB+VrrG7TW9wdhMPfmd+x1zNlTCzH/5D8PWBmEwbw2P5f5mBvFPwasCcJg7j6WjsA/tNYaM7kp1lr/2/X8pcCbmCW9UcDaxh7MoYkFdKXUBW5/LsVrrZ/TWr8OxCmlfq+1/h9m7QzMja5jlFJxrj8lp2itn6//XletlmP5O64/4zH/JB6qtX65nrtcrVqO5UXMrAmt9Vuu84Pm97QOY4lQSjV37bn7QCP/ucQCUVrr/2L+RfhSA3S7SjWMpYVSajrwFDAEQGv9GdDHddwW4NfBNBZfBM1/FF8opZoppb7ArPddjnnBZoVS6k7XId8BVyql4rXWZUqpC4DFmDMp8gG01qWVW65/PoxlN4DW+jut9Yn673llvvxcXFMXAfvaQIPyYSy7XBfg0VqXNUDXK/Hx53IKIFjm03sxluXANNfnFUqpR13HH3QdGzQ/F39oMjV0pVQa5o0oQzBvFIh3fd6LGbRPYWavPwKvYv45/35D9NUTGYuMJdDOsrEUYb4hfQ8kYl4I/V8DdDXgmkxAtyil/oZZ23tLKdUe88/3n4HfAP/WWh9qyP7VhowlOMlYgpOHsbzZWKby+qJJlFyg3FSjf2NOGWurtc7CnMu8EHNKYl4w1WOrI2MJTjKW4OTlWPIb83REbzW5DB1AKfV/QE8gB9gF/KS1XtOwvaobGUtwkrEEp6Y0lroI+nff2nDLJlIx58zu1lq/1Rh/oDKW4CRjCU5NaSy+aKoZ+jXAR1rroobui69kLMFJxhKcmtJY6qJJBnQhhDgbNamSixBCnM0koAshRBMhAV0IIZoICehCCNFESEAXQogm4qzagk6cHZRS3TC3GANzY+Y/uR6fh7lQE1rrOt01qJRKxtz84RvXKp0opRYAU4DBrqVahWgQEtBFUzdVKfUk5tKv1/mhvWTgUde/v/FDe0L4jcxDF02OW4a+G+gBjMbcP/IfmEumdsQsN/4Bc1/MBEADM7TWPyqlHsMM2q9h7jYUj7kf6FrOZP6WizB3wJmCuXHCRFfbN2qtvwvIAIWohtTQRVO2DViNWWaZhrmE6gnXc7di7pm5CTOwDwaWKKXC3c4fibl5SAvMLcuOYG6MAuZeoZMwt5WzDMNcarYT5o4+QtQrCeiiqZuPmTUPx9yqz3K56/P9Wuu/AUswF3Xq5XbMX7XWL2Jm+t1cmzuku57borV+u8KSrI9prZ/EXH+7m99HIoQHEtBFU/c2UAZkAF9U8bxR4bO7467PpZz5v1JTjdL9+NDadVMI30lAF02aa/u3acCdFbay+9j1+a+uJVevwrXcqocmc1yfRyqlblBKRfu1w0L4QGa5iCZPa/1OFQ8vwLw4ejvmRdO1mBdFS5RSNTW3AnP/ygtc53X2a2eF8IHMchFCiCZCSi5CCNFESEAXQogmQgK6EEI0ERLQhRCiiZCALoQQTYQEdCGEaCIkoAshRBMhAV0IIZqI/w9JE2DLbdFECgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = AirPassengersDataset().load()\n",
    "series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a training and validation series\n",
    "First, let's split our `TimeSeries` into a training and a validation series. Note: in general, it is also a good practice to keep a test series aside and never touch it until the end of the process. Here, we just build a training and a test series for simplicity.\n",
    "\n",
    "The training series will be a `TimeSeries` containing values until January 1958 (excluded), and the validation series a `TimeSeries` containing the rest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train, val = series.split_before(pd.Timestamp('19580101'))\n",
    "train.plot(label='training')\n",
    "val.plot(label='validation')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Playing with toy models\n",
    "There is a collection of \"naive\" baseline models in `darts`, which can be very useful to get an idea of the bare minimum accuracy that one could expect. For example, the `NaiveSeasonal(K)` model always \"repeats\" the value that occured `K` time steps ago. \n",
    "\n",
    "In its most naive form, when `K=1`, this model simply always repeats the last value of the training series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive_model = NaiveSeasonal(K=1)\n",
    "naive_model.fit(train)\n",
    "naive_forecast = naive_model.predict(36)\n",
    "\n",
    "series.plot(label='actual')\n",
    "naive_forecast.plot(label='naive forecast (K=1)')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very easy to fit models and produce predictions on `TimeSeries`. All the models have a `fit()` and a `predict()` function. This is similar to [https://scikit-learn.org/](https://scikit-learn.org/), except that it is specific to time series. The `fit()` function takes in argument the training time series on which to fit the model, and the `predict()` function takes in argument the number of time steps (after the end of the training series) over which to forecast.\n",
    "\n",
    "### Inspect Seasonality\n",
    "Our model above is perhaps a bit too naive. We can already improve by exploiting the seasonality in the data. It seems quite obvious that the data has a yearly seasonality, which we can confirm by looking at the auto-correlation function (ACF), and highlighting the lag `m=12`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jAEePHuXw4cMlq3N9rVaL8fHxysqbmpqqdXnrUWbV5dW9TdajzLqXB7fWLivW9Gn/LLYnLk7xzVfHV95K37PWT/fdZG1ZBjvIe5CyHZ07ul4FfuC7zlCVPSOXObjndGXlrUeZjUar0vI2w7+57uVB/dul7uXduQt2DF2u9L9hzWaz8v8m9hobG1vzWNlw9QLwMeBzwEPA8+0DIYS9wG8Bj8UYX1/twzHGY8Cx4u2GrHLRaDSu+Qu5WaOjo7Uubz3KrLq8urfJepRZ5/KWlvJ5SVnWYPfovcsTt/Oht05PUnuYbX6VIbf2bfftgJUBpy/u5Wsn78l7khqdHqWhxq1NxoaXOHvl3kr+zQBXFvZWWt56lNlqNQbu31z38qD+7VL38i5dScwt7a30v4nj4+OV/x24GaXCVYzxxRDCuRDCc8BJ4DMhhCdijI8BHwXuA349hADwCzHGL5etsKTrawek9jDbagGpPcTWno803zNxe64Jv/9HnYUM28FpqGfIbahx/SE3yM+5fZ/DbpK2vtJLMcQYP96z67Fi/6eBT5ctXxpEKaV8ocZ2KFrltv+ZuU4o+taZ/Jb/9vvlYbUVSzx3epGWJ2wPdYLSru35rfntXqSqF0aUpEHhIqLSOurtQWpP2p68kj/SY3YB5uZX9hzNzueLRnbf1db9/LH2IpHLt/wP5WXmt+nD6K5bHWaTJFXBcCXdgJQS8wtpRUhaLG7/n5tPzDc7d7PNNjtDbvncJTpzkIp0dPIcfPUbK2/5b88/Gt2d/7yZ9ZBGhmGHt/xLUi0YrjSQlpbSiseKtLe710eanst7lWYX4KVvwX97Ll0VklIrv+1/+Zb/oidp+wjs2rH22kh7dznkJklbleFKW0JKafnZazNzibMX0oqwNFO85or1kppLXUNuy4Xk+7YNd+5k2zacLwGwt+Lnm0mSti7DlWotpfzhtQtFcMofS5KYnoMrs3nv0sxcHpxaxaNGXj0NX3oxXTMs3cpq25Ik3QjDlfqmuZiYX8gXkhx/IzHfTEzPFqFpFqaLXqb2HW5Zlp+bZUVQGoLh4XwIbveOziTuPfYySZL6yHCldbPQTJ11lBbg8mxiahqmZmBqurjDjfzYc1/LB+eGh/PepW1DsHN7fudbmQfdSpK00QxXuiXt4bp2eJqdT1yeyYPT5eK1fKccee/T8FB+V9vINti/B4aGivWUGnDwdgOUJGlrMFzpmpqL+WKVM3MwOZ2YuAwvn0o89b/SiongkPc6jRQ9TwdGndckSRpMhisB+STxmfl8rtOlK4mLUzBxOZ8o3l56YKiRz29qZHDnbS5UKUnSagxXAySlfA7UzFx+l93Fqbwn6uLl/C689rIEw0OwYwR27oB9e64OUMNDBitJktZiuNqiWq18DtSlK4nT5xP/849bXLwCS0vki1+mfAhv+0i+IvjwkGFJkqQqGK62iIVmYnI67406fQFev5gvWwAweSVfJ8p5UJIkrT/D1SaUUr4e1NQMvD6Rh6nJ6WItqAbs3g637+sEqZ3bYdcOQ5UkSRvBcLUJLC4mpmbg0uXEmQk4eyFfIyqRD+3t3gkHD7gelCRJdWC4qqHZ+XyxzfOTiTPn4cJU8aBg2pPMnSMlSVJdGa5qYm4+n3j+ymuJ33muWK18KH+si8seSJK0eRiu+mhpKXF+Ev7ydOLbZzurmd/jauWSJG1ahqs+mJpOnHo98Y2T+aNjdu2Au/fnvVPbhvpdO0mSVIbhaoMsNBNnJ/JA9cYlGBqC/XvhwKi9VJIkbSWGq3WUUmJiCl49nfjWmXwBz7274d47DFSSJG1Vhqt1MDOXGH8j8Rffzh8zMzK8ct0pSZK0dRmuKrK4mHjjErwynhh/HRqNfMmE0d0GKkmSBonhqqT5hcSffrPFK6/lDz/evRMO3u6CnpIkDSrDVQkpJf7yNHzjFNy2B7YNG6gkSRp0hquSWi248zZDlSRJyjX6XQFJkqStxHAlSZJUIcOVJElShQxXkiRJFTJcSZIkVchwJUmSVKHSSzGEEB4HDgEngEdjjM1i/xDwG8DbgK/GGD9a9lqSJEl1V6rnKoRwPzAWY3wQ+DrwcNfhnwROF8d2hxB+uMy1JEmSNoOyw4KHgGeK7aeBB27wmCRJ0pZUdlhwP3Cm2J4EDvQcm1rjWF8sLCxw8eJFXn755UrKSylxeeoCp75dTXkAk5eqLW89yqy6vEsXL9a6futRZt3Lg/q3y9zSa+yocf3Wo8y6t8l6lFn38qD+7VL38qZmEpOXLlT2txng3Llz3HnnnYyMjFRW5s3IUkq3/OEQwj8CrsQYPxdC+EHgIzHGo8Wxfwv8bozx2RDC3wLuizF+pufzR4AjAEePHv3Bw4cP33JdbsSrr77K+973vnW9hiRJ6r9nn32Wt7zlLetW/tjY2JrPvivbc/UC8DHgc8BDwPM9x94PPFsc+4+9H44xHgOOFW9vPeXdoDvvvJNnn32Wu+++u5LyUkp84SuJO3y2YCl37Hqd8zN39bsa6mG71I9tUk+2SzlTM4m798P3v7W6BQzOnTvHe97zns3ZcwUQQvgV4L3ASeAjwGdjjI+FEIaB/wC8Ffi/McZ/fJ2i1j1cAYyPjzM2NlZJWSkl/vMfJA7ebrgq4+Ce05y9cm+/q6Eetkv92Cb1ZLuUc+lKYuwOePf3Vheuqvxbfw3r1nNFjPHjPbseK/YvAo+ULV+SJGkzcRFRSZKkChmuJEmSKmS4kiRJqpDhSpIkqUKGK0mSpAoZriRJkipkuJIkSaqQ4aqkoSE4P5lYWtqQNVAlSVLNGa5KyLKMwyHjvnvgwhScOZ+4PJMou+q9JEnavEqv0D7obtub8YPfk/H9b02cvZB4+TU4OwGNLLF/L4xs89E4kiQNEsNVRbYNZ3zn3RnfeTdcmUmcfD3x8im4MJXYvg1u2wONhkFLkqStznC1DvbsynjHmzP+ypsS5yfh1fHEt89BKyX27YZdOwxZkiRtVYarddRoZNy1H+7an/EDb0+cPp/4xik4fT6xbRj274XhIYOWJElbieFqg2wfybjv3ow335OYvAInz+Xzs5qLiV07YHRXPkFekiRtboarDZZlGbftzSfCv+PNiTcuwcunEqfPQ6OR2LcHdowYsiRJ2qwMV300PJxxzx1wzx0ZM3OJ115PfP0kXLqSSAm2DcPuHbBjxF4tSZI2C8NVTezakfH2N2W87TsTM3MwOQ3nLyXGz8O5CSBLZBns2g67dsCQdx5KklRLhquaybKM3Tth9064946M7/9uWGgmpqZhYipxdgLOXYSlpUQCtm+DPTtdT0uSpLowXG0CI9sy7rgN7rgt4+1vglYrcWU2Hz48NwFnLuTraWXkj+PZtSPv4XIoUZKkjWe42oQajYzR3TC6O+NNd+f7Zufz3q3zk4kzF+D1iyw/hmfH9nze1vZtBi5Jktab4WqL2Lk9Y+d2uPtAxjvvg8XFxOVZuDiVL2R6YWpl4CKDkeEidI04h0uSpKoYrrao4eGM/Xth/96Mt4zl+1qtxOw8TM/B9Gxi4jJMTOWvpaUEGVDcpbh9BHaOwJCLnEqSdFMMVwOk0ehMlmd/xn3F/pQScwswPQvTc4lLl+HCZbg4BQuL+VwuMhhqdHq6tg05xChJ0moMVyLL8iHFndvhDjK+62Dn2PxCYnoOZubyCfQTU3DxCkzMA6R2ZxckGNmWDzWObMt7vwxfkqRBZLjSNW0fydg+AgdG4Tvu6oSlVivv7Wq/2hPqp2bg8jRMXM57xJY/keW9XduGOyGs4TwvSdIWZLjSLWk0snzJhx3tPSuDUkqJ+Z7wdXmmCF8zcH4SllqJRga374AzF/LtbcP5a3goD2DO+ZIkbTaGK62LLMvyJSC2L+9ZcTylxEIzD16XJuBHvi9jdj4fgrwyA9Pz+Sr188289yvLIKV8CLIdwoaHusOYIUySVA+GK/VFluXDjdtH4MokjN2d0RvAIL+LcWERFpow36QIZHkv2PQczMzD1HQ+8T6lPIRRhLCsmIQ/PHT1y/lgkqT1YrhSrQ0NZewcyifbd1wdjFqt1AlgRRhbaCZmi7sg5+ZhtpkHsrkF8m6w7uISy3dEGsYkSWUYrrQlNBq9w5CwWgiDfEhycQmaRQhrFtvzC3kYm52Hmdk8jM3M5+9Tyh+cvbIclh85NNTIfw4PwXCjs89QJkmDx3ClgZNl2fJcrc6EfLheGGsHsvbP/JUHsvkFloPZQhOmp/MetBsKZY2VAS3fZyiTpM3KcCVdR3cYWzk8CWsFMrg6lK0MZp1QNl8MZ851hbPFpatDWXvoEvJJ/e0hzJWhzDssJanfSoWrEMLjwCHgBPBojLHZdeyngE8CTeCrMcafL3MtabO51VAG+Ryy7p6y7ldzMZ/UvxzMFvIhzPb7xcW0PKG/faX2nZZwdW/ZcFcwaziUKUml3XK4CiHcD4zFGB8MIXwCeBh4suuUrwEPxBgXQwhPhhBCjDGWrK80EBqNjJFGvuDq6tYOQO1g1hvI2tvzC4n5Zt5L1t1zNrUAi4uQyHvNbt8JZy/kkawd1lbtMXMoU5JWKNNzdQh4pth+GvgIXeEqxniy69wFoFXiWpJuUJlg1j2U+cbr8IEfylaEs/mF/OHfvT1mcwv5orB5IZ1es9Qeykwre8qGu3vNHMaUtMWUCVf7gTPF9iRwYLWTQgjvBu6KMf7JKseOAEcAjh49yuHDh0tU58Y0m03Gx8fX/Tq6cbZJPbWWmsxePr38fpg8DO3etfZnUuoMQba3AVrFdqu1+nbnIZV0sl9XSMsylh8g3l5UdhBtG2pycM/p65+oDWW7lHPnrnz6RJV/Bjbi78rY2Niax64brkIIB4HfWuXQM8Bosb0PmFjls98B/CrwM6uVHWM8Bhwr3qbVzqna+Pj4NX8h2ni2ST1tVLt0zy9rLuZLYywWP+fm8yHMmfnO3ZjzCzDXzMNZd+BqB7r2nZhX95Bt/qHLg3tOc/bKvf2uhnrYLuVcupIYuwPe/tZGZWX2++/KdcNVjPEs8GO9+0MI7wI+BnwOeAh4vuf4XvJQ9liM8fUK6ippC7r2MObaYWhxMS2vUdYdzhYWEzNzLM8rmyuC2dx0PnTZDmDLxRc9bd1zyRqNqyf+O9Ff0o265WHBGOOLIYRzIYTngJPAZwBCCE/EGB8DPgrcB/x6CAHgF2KMXy5fZUmC4eGM4Zu8E7PVSit6yLqD2ez8yjsw2xP9p+euXrOsa+RyxVVXW7us0YChzDsxpUFSaimGGOPHV9n3WPHz08Cny5QvSVVqNDrPtLzatSf6L3XdfbnUunqJjPZdmHNdwSxf+b8T5lJ7QtpaIa0rrTWKYNa+Q7PRgLt25yGwkUFWHGtk+b9LUn24iKgkXUeW5b1kw9f8L+b1A06rlVhqwVIR0JZfSyu3F5dSMcTZPdxZBK2h4pwitOWfSZ3M1nNDQEor9y2vgZba/7Z2r1pePqx8n2Urt7v3dd4b7qRuhitJ2iCNRkajkd8ZdW2rh5XxcfjA266e9JtSWr77slWEtPb7FdtLK89ZaiUWF2GxOGdpKd/uDX/t3rpWqxhK7TqnlSC1UudmzyLQtQPc8rIcdBa0zbIi2xU72/PgenLg8l2i3WGu+27R5UDYfV7x6+sOiys+t9q5y9cwJKoahitJ2uSyLMvneN38Jyu5fne4ay+zkSh+ppXb7XO6t9c6v5Xy8NdKnVDYPr8dCNvlrBYsUytfYLFVBMRWzzVb3fVoQac7r/0Pu/avqR0Wb98JZyfSqqe1lyNZ8aHlg1fP2+sUzHLwg064XN6m5333UPMaYfOqz7TfGzgrZ7iSJJVy6+HuuiVXXuK1pCIJpa4AmG7g/cUL8OPvyTqfXeMz1ysbVu5rtUNra2WAbJfTu783aMLKgNkbOFsJWtcNndcJnF3r1K2Ww1YMS3ef3/U7mW/C2B030VCbgOFKkiQ6vTQ321kzdRH27VmPIFiPXqOU0pqhsLsncrXA2LrBY3uvsTjxZmS4kiRJa8qybGCfinCrqlsOVZIkSYYrSZKkKhmuJEmSKmS4kiRJqpDhSpIkqUKGK0mSpAoZriRJkipkuJIkSaqQ4UqSJKlChitJkqQKZe0HVUqSJKk8e64kSZIqZLiSJEmqkOFKkiSpQoYrSZKkChmuJEmSKmS4kiRJqtBwvyuwkUIIjwOHgBPAozHGZn9rNNhCCG8G/hj4s2LXz8YY3+hfjQZXCGEf8EXgHcB7Y4wvhRB+FvgnwCzwczHG1/pZx0G0Rru8AowXp/xyjPGLfavgAAoh/BDwa0CTvB3+HvA38LvSN2u0yZ/Tx+/JwISrEML9wFiM8cEQwieAh4En+1wtwZdjjA/3uxJiBvgJ4FcAQgjDwMeAHwXeDXwKeKxvtRtcK9qlMBlj/LH+VEfAKeCvxhhnQwj/Gvhp/K7022pt0tfvycCEK/Ieq2eK7aeBj2C4qoMHQgjPAc8Bn4gxuqptHxS9uG+EENq73gb8RYxxAXg+hPCZvlVugK3SLgB7QghfJv+/8qMxxom+VG5AxRjPdL1dAL4Hvyt9tUqbtOjz92SQ5lztB6aK7UngQB/rotwZ4LuB9wF3AX+zv9VRl+7vC8BQvyqiqzwQY/xR8v9J/MV+V2ZQhRC+C/gA8If4XamFrjb57/T5ezJIPVeXgNFiex/g/+31WYxxHpgHCCH8V+C9wOf7Wim1XaLzfQFY6lM91CPGeKHY/G3gH/SzLoMqhDAK/CbwCHmY8rvSZ91tUvT49vV7Mkg9Vy8A7y+2HwKe72NdBIQQ9na9fRD4Zr/qoqu8AnxvCGEkhHAI+NN+V0hQtMf24q3fmT4o5iP+FvCLMcZv4Hel73rbpA7fk4HpuYoxvhhCOFfM7zkJOC7efz8SQvgl8km73yKfCKo+CSF8AXgX+RySJ4BfBb4EzAE/1696Dbqedvkd4EMhhGnyXt9H+1ezgfVh4D3Ap0IInwL+PX5X+m21Nvmn/fyeZCk5f1iSJKkqgzQsKEmStO4MV5IkSRUyXEmSJFXIcCVJklQhw5UkSVKFDFeSJEkVMlxJkiRVyHAlSZJUof8Psnv5Txu/WUYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(train, m = 12, alpha = .05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ACF presents a spike at x = 12, which suggests a yearly seasonality trend (highlighted in red). The blue zone determines the significance of the statistics for a confidence level of alpha = 5%. In cases where we are unsure, we can also run a statistical check of seasonality for each candidate period `m`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is seasonality of order 12.\n"
     ]
    }
   ],
   "source": [
    "for m in range(2, 25):\n",
    "    is_seasonal, period = check_seasonality(train, m=m, alpha=.05)\n",
    "    if is_seasonal:\n",
    "        print('There is seasonality of order {}.'.format(period))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A less naive model\n",
    "Let's try the `NaiveSeasonal` model again with a seasonality of 12:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seasonal_model = NaiveSeasonal(K=12)\n",
    "seasonal_model.fit(train)\n",
    "seasonal_forecast = seasonal_model.predict(36)\n",
    "\n",
    "series.plot(label='actual')\n",
    "seasonal_forecast.plot(label='naive forecast (K=12)')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is better, but we are still missing the trend. Fortunately, there is also another naive baseline model capturing the trend, which is called `NaiveDrift`. This model will simply produce linear predictions, with a slope that is determined by the first and last values of the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drift_model = NaiveDrift()\n",
    "drift_model.fit(train)\n",
    "drift_forecast = drift_model.predict(36)\n",
    "\n",
    "combined_forecast = drift_forecast + seasonal_forecast - train.last_value()\n",
    "\n",
    "series.plot()\n",
    "combined_forecast.plot(label='combined')\n",
    "drift_forecast.plot(label='drift')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened in the last cell? We simply fit a naive drift model, and add its forecast to the seasonal forecast we had previously. We also substract the last value of the training set to the result, so that the resulting combined forecast starts off with the right offset.\n",
    "\n",
    "This looks already like a fairly decent forecast, and we did not use any non-naive model yet! In fact - any model should be able to beat this. But hey, what's the error we are getting here? Let's see what we'll have to beat:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute percentage error for the combined naive drift + seasonal: 5.66%.\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute percentage error for the combined naive drift + seasonal: {:.2f}%.\".format(\n",
    "      mape(series, combined_forecast)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quickly try a few more models\n",
    "`darts` is built to make it easy to train and validate several models in a unified way. Let's train a few more and compute their respective MAPE on the validation set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model Exponential smoothing obtains MAPE: 5.11%\n",
      "model Prophet obtains MAPE: 9.80%\n",
      "model Auto-ARIMA obtains MAPE: 11.65%\n",
      "model Theta(2) obtains MAPE: 8.15%\n"
     ]
    }
   ],
   "source": [
    "def eval_model(model):\n",
    "    model.fit(train)\n",
    "    forecast = model.predict(len(val))\n",
    "    print('model {} obtains MAPE: {:.2f}%'.format(model, mape(val, forecast)))\n",
    "\n",
    "eval_model(ExponentialSmoothing())\n",
    "eval_model(Prophet())\n",
    "eval_model(AutoARIMA())\n",
    "eval_model(Theta())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we did only built these models with their default parameters. We can probably do better if we fine-tune to our problem. Let's try with the Theta method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Theta method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model `Theta` contains an implementation of Assimakopoulos and Nikolopoulos' Theta method. This method has known great success, particularly in the M3-competition.\n",
    "\n",
    "Though the value of the Theta parameter is often set to 0 in applications, our implementation supports a variable value for parameter tuning purposes. Let's try to find a good value for Theta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search for the best theta parameter, by trying 50 different values\n",
    "thetas = 2 - np.linspace(-10, 10, 50)\n",
    "\n",
    "best_mape = float('inf')\n",
    "best_theta = 0\n",
    "\n",
    "for theta in thetas:\n",
    "    model = Theta(theta)\n",
    "    model.fit(train)\n",
    "    pred_theta = model.predict(len(val))\n",
    "    res = mape(val, pred_theta)\n",
    "    \n",
    "    if res < best_mape:\n",
    "        best_mape = res\n",
    "        best_theta = theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MAPE is: 4.40, with theta = -3.5102040816326543.\n"
     ]
    }
   ],
   "source": [
    "best_theta_model = Theta(best_theta)\n",
    "best_theta_model.fit(train)\n",
    "pred_best_theta = best_theta_model.predict(len(val))\n",
    "\n",
    "print('The MAPE is: {:.2f}, with theta = {}.'.format(mape(val, pred_best_theta), best_theta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/DKAoigX4OTAPmAHcA/wUeAi4FigG3gdE0AVB6JTico15t2hEWKH8HTCbgyfq4VW64EYP1+PnlmQb7sO1aOWBe83FFXoTCwBbalsP3TzECkegoo8F3d+Qy2xFUf6rKMpDwBhgt6qqTaqqbgQme44ZpqrqflVVq4FyRVGCsLhWEITBzMr3oNFTX6U2yBkhkdV6yCU+S/egrWm68IZVBEHQy/V4PIAtta2HbjSLrjkaeh56IZAD1APPAucD1T77wzyPvh8OVUAiUOo7kKIoNwA3ACxbtowzzzyzZ1b3EQ6Hg4KCgv42IyjIXEKT43kuLjc8vSYFQ4b25hYxLMkdFFs0DWIbPDdF42spKCjAHafXOQqraujSzq7msjs3kiEu/YOhiiocrTzxsAT9WnWF9UF/fTtb8NSloKuq2gg0AiiK8gZwFeCbyWnk5fi+EvFAeTtjrUCPs0OIrhT1JdRWvgWCzCU0OZ7n8v5mjSPHmmUgKjadjIzghFxq6zUSm/YDMHr6cKIyIikf4+AIx4hqcHRpZ1dzcaCR7vHQh5+YScSwluUbM8aCnXxMNe4+fX27DLkoihLr83Qu8C5wgqIoVkVRTgW2efYVKooy2nN8oqqqpa3HEgRBMHjmrZY+XTC7CZWWu0n0CG6E56ZlwnD9Mbq+KeAmF8VlbuI841tT2sbQ07L1beY+LtDlT8hljqIoD6KHXL5DvwlqB9Z7Hq/0HPc/wCr0EMx9wTZUEITBQ0mFxjufQ7gFJmTDtweguj5445fmNRGGRo3Vitmq+63RGboXneBopN6u10jvKZUFTYQB7phwzOFt/eKho6wcAiIbmmhyaFjDg5zB0wH+hFzeR89a8eVVz4/vcbvQPXhBEIRO2XsY3G44+QTITPEIehAbOJfn6fHzuhifOuWem6KJTr2eSyCCXl+ke96mxLbeOTQv/493NlFwDEYO6/m1uoMsLBIEoc8pLNMfhyXpnYSAoBXNAqg5ot+wbIrzFXT990RnI5UBrha1lxjhlvbLWFs9XYvinQ4OFffd7UIRdEEQ+pzCMoh1NpEV2UScR9CD6aE3eOq4uIY036y0RFuwWyyEaxqVRYF19nJ7Fi1FpbfvoYfZzDRZLVjQyD/Yd13EQrZjkSAIg5fSw42s3L+ByEdd1A+LZbc7k+r6TDop9d0tHEW64JpSWmaf1EdZiah2Up3fCLQvxl3hdjcv+zdK5baHMzYca5mTou+aenyt7iIeuiAIfY59Xy1RbhcmDaILalhauIfqyuDkoAO4S3UPPbzVop/GWP15Xb7/i4tqD9Sx6+7dNHmKcZVVQ3wny/4NzAn6vrLDfVfPRQRdEIQ+p+moZ1no7FRcCTbC0HCUBE/4wip0QY9slR/u9MTUG4r8v1beXw+R99fD5D7+HdB6lWjHnrctORyAmsK+S10UQRcEoc8xeTzomJxoSNVF16iOGAysRiXE4S09aHeCLsCOY/4LemOxfmzhm0VomkZRF8v+DaI98fWGEhF0QRAGMTaPB504OgKLR9BNQaixAqBpGlF1+lhJ2S0FNyxZv5ar1P9rNZXqgmzPt1OpVvHyRxoJRnPoTjz0IcP1fa7ywBcy+YsIuiAILXA4HKxevZof/ehHjB49mi+//DKo4zc2acTV64KeMiYCq6e8bXhFcDx0Z40Lq8uF3WRm+KiWeR8WT5phdz48GkubPew9Lxbyj3/756HHeeqkR9odVAZxFWxnSJaLIAgtuPXWW3n66ae9z9966y1OPvnkoI1fVA4pnubNUVlRRA+roxGIqA6Oh153VB+7wmJjWHLLrJkIo+JiVTc8dJ+GGAVvFmMaPoZ4lwPMYE3qLIbuKTngbOJwMQyJ7fDQoCEeuiAILTA88lmzZgFQXFwc1PGPlmpeQY/MiCDGU942qi44HnrhPl2sayJthFtaCnr0ME8p3Vr/4tquRjfOaicmiwnLsAiiahuZXVeij5FsxRTWcZpl8+IiXdD7AhF0QRBa8N13ejbHFVdcAQRf0Iu+c2DT3NhtFiyxFoZk63HtuIbgeOgluW1XiRqkjNS3RdT7J+hG/NyabKXkxHQAbi/cBTTXPO8Io2hXvKuJ/GN+XS5gRNAFQfBSXV1NeXk5ERERTJ6s964JtqCXHdBTFhvjPcWyPDcuhzTacToDv3lYfsjzwTCkraAPy7bgMJmIcDhxNbja7G+NIei2FCtbJo5gT2Q84Q79vM7i5wA2j4ee4GyitKo7M+g5IuiCIHjJy8sDIDs7m6FDhwLBF/TqPF3Q3Z6Mk4ihepWsRGcT1bWBC3rdUV3QWy8qAhiaZKIyzJNOWNz1N4ImTzaMNdnK7iorvxg5A+evp5IyP5kR12Z1em54op6HHutyUFoevEVTnSGCLgiCFyPcMnLkSNLS0gBd0IOZdtfoqbNi8Qh5mM1MdbiVMDTKDgUedmkq1sc34uW+2Kwmamz69pKDXV+r8VhzyOXgUcBkYtT5qcx4bTppC1M7PddsMevldYHqYkf3JtFDRNAFQfDi66FHRUURExNDY2Mj1dXVnZ/YDTTPAqKozOZVnNVRushWBUHQ8SzRT8hqPyRij9Y99JLvuo6jGyGX8CQr3xXq20Z1oxSueYh+LXtx3ywuEkEXBMGLr4cOtPDSg4XFk28+ZFRzQfL6aF18a44EnukS7kl/TB3dvqC74nWRrfCjxooh6A2RVhxOSE+EqAj/C4iFe+LoTWUi6IIg9DF9IehR1bpop45t9tAb4/Tf6wsC99CjPdkyGePaF3RzoufDo6BrkTVCLhVmXZhHd7M9aKQn08VdLoIuCEIfY4RcWgt6UVFRUMZ3OjWG2HVBH3ZCs6A7EvTfmwoD89AbqxxEeFaJZma3v27SSDds8KMYmOGhF7k9gt7NzkMxntWiYdWNaJpGxVeVfHmBSu4T33VvID8RQRcEAdBroBgeenZ2NhB8D72o2MUQZxNOk8nb4xOAJN1rdnaz4mLh2iKqd9Z4nx/dq59fbbMRGdG+vEV7Sg24Sv330I806hkro4Z1r157jOc+QZy9kboGqNtfR+n6Mmp8bA4mIuiCIABQXl5OTU0NsbGxJCYmAsEX9ILdugdeHRmBydwsjmajlVup/x56XW4dW6/+FvWSLWguPQvn6H5d0I2YfHvEeyowmir999AP1PYs5BLhqZee5GyktArsnm8gEUM7z2HvKSLogiAALcMtJpMutsEW9LL9uqA1xLYUNEu6foPU3I0CXfWH9Xx2+1E7pf/Rm5SWfqeLtKudVaIGKdm6OHe1/F/TNK+g767sWcjFlt7cx/RYJRTs1ee3pVwEXRCEXqR1uAWCL+jVh3VBc/j0+gSI9His1qpGv3Pem3zqjOe/XABAlac5tCm5Y8EcmuOpHdPQ+bWcNS7cjW7CosPYWxIGdC9lESBiqD7PRIfuoZfn6fZ9WyGCLghCL9I6wwUgPV2vXxIsQbcX6oKmJbUU9NgkC7VmC2FON44K/xbhNPo0qSh+twRHpYMGI6TRSWu4YcPDsJvM2NxunDUdN3A2VolakqxU1EB0JKQO8cs0L74hl2OV4PCsTk3IFkEXBKEXaZ3hAsH30F2eVZyWtJaCHh8NZeG6yNmP+hd2afQpa+tudHN0TREuz7bYjI4FMybKTJXVs1r0u47j6Ea4xRXTHG4xQlH+Yom34LKYiXK7KC92elvjpedEdHFmzxBBFwQBaN9DD/ry/7Lmsrm+xEVDmUUX2UY/aqxAc8gl6Xv6Ddy8vx4ipljPHkkc2bkHXB+pi3RRbsdxdOMDw+45trvhFtA/AJyeZtE1hxqw1TfhBrLGi4cuCEIvkp+fD8Dw4cO922JiYoiKiqKhoYHa2sDb7oR7Mkta9/qMi4Jyw0Mv9E/QjZBL1tXDsaXbqNtfR3JVHdAcJ+8Ih+embGe1YwwPvSq8ZzdEDTTPQiYttwazBlUWK6Ozekd6RdAFQQCgpERv3GB45QbBDLtE1XqW/Y9sFXKJafbQ7UX+CrouuJHDIzn1g5kMvWEEtWYLJeERjJoW2em5mqdZdNWRth76E/908rNf2ajYo384lLj1HPSczO6FWwzCU/S5hh/Ua+iWhdvITOnRUF0igi4IA4jNmzdTWloa9HHdbrd33OTk5Bb7giXoLruLqEYHTkze1EGDuCgoN0IufnroTceaGzVHZkRQdO44fjJ+Hn87bzZRcZ131zRqrNS1+vAoLHVT9+utXLfuawpWHAIgt14/dspov8xqg3FjNOqIXuCsIdqGxdKzD4euEEEXhAHC7t27mTVrFjNnzqSmJrgrDSsqKnC5XAwZMgSrtaXYBmv5f6NHPMvDbaQMaSlo8TE+IZeirm+Kam6NplI9G8bqSVHcuF3DZTIza3LXshaRpp/T1Gpl6kv/W8iU2nLsJjONsVZswyJY15SEyQSTeyjosZn6tbJq9dfMndg78XMQQReEAcOWLVsAyM3N5bbbbgvq2Ea4JSWlbSwgWB56Xb6+EKg03EZiq4bJEVao9GSeNPjhoTeVO9BcGpZ4C2E2XcY27tD3zZ7Utfcb58mC0SqaQy6lRQ7S39wPwF+HjudPi+aR+Pr3+C48hrHDITqyZ161kaJo0/QmF9a03slwgW4IuqIolyqKcszz+4WKomxSFOVjRVEyPdvGK4rymWf7/N4yWBCOVw4cOOD9feXKlbz++utBG9sQ9NTUtk0bgiXoZZ6GEtWREW1CDiaTCUe8/zF0I9xSY7Xy5n81HE6NL3fr+06d2LUtiVn6txBLdfO11v7sIImORgqGxPFhwjC+3gdfeMY8aUzXY3ZEUquMm5jMfhZ0RVHCgAuBI4qiWICfA6cB9wL3eA57CLgWWAD8JuiWCsJxjiHos2bNAgiql96ZoAdrcVHld+0v+zdwe9L7HMcavbVZOqLRk7J4oM7KpQ9ovL0R6u0wJhNSh3TtSWeM10U1qtaz0EnTiPuPnuWTff94Rme4aGyCVe/rdkzN6XnMO2pYSwFvLfDBxF8P/VJgNeAGxgC7VVVtUlV1IzDZc8wwVVX3q6paDZQripLcwViCIPQAQ9B///vfExERQX5+flBSCQGOHdPb0nfmoQcaQ6/xLMt3JrTvoSYOMVMRZgV3y0VD7WGkLFZabNib4Jo/6MLrj3cOMPJEGy5MxDc10VTvovyogyiXkwZzGPN/Es+0Mfr1v9ilHx+Ih25Layngwzqo0x4MuhR0j3d+EfCqZ9MQwLcfVVg7Y1UBicEwUBAEHUPQx4wZQ2ZmJgBHjhwJytidxdCNZtHdEXRHpYOKrypbbPOuAE1uX9BHpPvmond+Y7TJI/iVFk/6oedzzZ/4OUBEpJkqT2/RvJ12vtvWXAXSbDYzLadl+YGpAQi6JcaCPbw562bUxN4LuXSe26OzBHhNVVW3oigAlUCcz36X59G3rXU8UN56IEVRbgBuAFi2bBlnnnlmD0zuOxwOBwUFBf1tRlCQuYQm/s6lqqqK0tJSIiIicLvdpKamcuDAAbZu3UpcXFyX53eFsUrUarW2scdY7p6fn9+prb5zOXxPPhVvVZL9eBbxp+v2NRbWEQU447V2x0mKjqXcYmM0NRzdeZS61I6/fZQe1KsrVlisRFg17E26jTmpJRQUdFyfxZe6aCuJjXa2f15CQ4WbRKAhNpyCggImjQRdxiBtiAtHfQkF9X4N2y71UeFEVDlpMplxhJVQUNDzEE5GRsc1fP0R9AnASYqiLEEPt/wMOEFRFCugANs8xxUqijIaKAESVVVtkyyrquoKYIXnafDaiPcSBQUFnf7xBhIyl9DE37kY3rHhnefk5LBp0yYaGhqC8reor9fVauzYsW3GM2qjHzt2jGHDhnVYz8R3LnmHDgNQ/VoNE5acAEB4pZ5BkjgqgYyMqDbnn5ijsd+Tix7tiOl0XqUNur9YYbGy7HwTb20AazjMm5GK2eyfWLqTjkF5NU0lNhyV+odAWFoUGRkZOF0FREdCXQNMHxcW8N/YlXAUqhqoibQxfHhmQGN1RpeCrqrqncbviqKoqqrepCjKxcB6wA5c6dn9P8Aq9BDMfUG3VBCOY4xwS05ODtC8PL8vQi6RkZHEx8dTVVVFRUWFV+A7w6jHUr6xgupdNcSMicZS04ib5rzs1mSnwxeekEtjF7nozSEXGyOHmti+CsLM+C3mAOHpNtgPVYfsOKqdpAERnhuYljCYMR7Wbw0sfm6gJdrgEDTG9178HPzz0L2oqqp4Hl+lOaZu7NsFzA2eaYIgGPSVoLd3UxT0OHpVVRWFhYVdCrrm1rxZKACHnjtMzu2jMGlQbrGSnNT+rbsR6f4v/zdumlaGWUlJAJu1+yGM6OF6eYDGo3bctbqHHp/dHN++eqGJHQc1fnxa4Ks6I4fZYCuEp/Ze/BxkYZEgDAh6W9A7y3KB5tTFwsLCLsdqKm1Cc2mYPSJ79LVCanbp8fDS8AiS49s/b0Ra803Rxi4E3VjhWWGxdjheVySN8iwuKrVj8ZS1TRvbLLhXLDBx7G0zU8cELuinLIgBQPlBTMBjdUa3PHRBEPqH/fv1+HNvCLrT6aSsrAyz2dyh921kuvgj6Ea4JWpUNLZUK2WflfP1FVsBKLNEcGpC++fFx5ho8rSOqy3oWNA1TaOxtDnLJaWD8bpi2PgIDgO2Sjs2h+6hZ03qvKhXTxlx8TASxscQNzG264MDQDx0QRgAdOahB1qn3CjKlZSURFhYWLvHdCd10e4R9Ih0GxMeGk/M+Bg0h25jgS2qU486OqPjtEV7USNf/lhl4/zNaA6N+jALDnNYjz30rBN1bzyuoZHEJt3m1DG9E+M2hZlImBaP2dq7kiseuiCEODU1NRQXF2Oz2bz55/Hx8cTExFBbW0tlZSVDhnSzN5oPXcXPoXshF8NDt6XZiD0hlrkbTqVmdy2XX17JJ7ZUHutEgJOHW3FhIqzSgavR7a3TArDrrt2Uflrmff6dTQ9fJPVQ0GMzInCZTAxx6d5+fbiF8Jjwng0WIoiHLgghTm5uLgCjRo3CbNb/ZU0mk9dLP3z4cEDjdxU/h26GXIqaBd2w1ZYTw9roDOy2cOKiOz43a5iJCs9iId9KiMXrSih6u5iw6DBOfl3hhFdmcF/WSSTEQHgPS9GawkzURjZ75A1xvXvDsi8QQReEEOfgwYMAjB7dsn5rsOLonaUsGnQr5GIIuk+j5lK9twPJ8Z335cxONzX3FvWEXZy1Tnb+Sq+SNfbuHJJPS6Jx7BAawiw9jp8bOBKabXQniqALgtDLGKsqfVvD+T4PlqAHO+QS0Y6gdyXAI9L0G6cA9qP6OPn/LMBeYCduShzZ149oMV5P4+cGppRmEbcOFUEXBKGXOXr0KADDhrVsahksQQ96yKW4ZcgF/BfgEelQEq4La/1hvX567T69FVzGRUMxhene/bFK/fhAPfQIn0qIsVki6IIgAB999BG///3vcbvdXR/cTToS9KysLKBvPHSjk1F1dbW3TEBHtCvolfpjckLntoxIh2KrnjrYcEQX9AZPY4zI4c0phcHy0BN8FhIl54igC4IA3Hrrrdx99928//77QR+7tz10f2LoJpPJG3bpLI6uaZpX0H/4x3COlurpiof1S3QpwMnxUBWtC2v1dx5B93jqUVmR2Bs13G4taB6670KizAki6IJw3KNpGnl5eQCsXr066OP3laB35qGDf2EXR4UDd5NGY7iFT3aG8dhqXdD/tV5/nDu584wUk8mEJV0X1tpDDWiaRkO+fnP0kBZBwmKNu/6qcaxSHy85PrBVnBknNIv4kFG9s6ioLxFBF4QAqaio8IYh3nzzTRob/eta7y9dCXp+fr7foR5N0/jjH//Ixo0bvdv8iaGDf4JueOcVnkyVle/Btly9PVxcNJznR7WnmGxdWB1H7TjKHbjqXFhiLby5xUJjEzz7NhR60tED9dCjfdrBRQwTD10Qjnt8PeSqqio++uijoI1dX19PZWUl4eHhJCUltdgXFRVFYmIiTU1NXlHuis2bN3PnnXeycOFCvvvuO/Lz88nP11uvdSXo/oRcjJTFEpOeS15WBT/5je5NX3gaRNq69qjTMi3Um8Mw2V1UbdN76URmRfCfb/X9lbXw/hf6713F5LvClmYjaU4i6eektVjENFCRlaKCECCtQx6vvfYaixcvDsrYhjfcUR3y4cOHU15ezpEjR7yt4vyxtaamhiVLluByubDb7SxYsICEhIROz+2Oh25UTQTYqffO4PIf+BceyUo3UxIeSXZjLeWbKgCwZUSyaUfzMdV64gspgaYtmk2c8taMwAYJIQb+R5Ig9DOGhztnzhwA3nrrraCFXXwFvT2624rO17vetGkTX3zxBVlZWTz//PNdntutkIvFxrgsiPGEpUekw9zJHZ7WguGpUOxJXSzfpDeyqI6JpKGxeTyDQD30wYYIuiAEiCGm8+fPZ/LkyVRVVfHpp58GZeyO4ucGvnF0fzAEff78+fqSfJuN119/vdMMF4POFhcdXVPIsX+WcewTvdBXmcXGhBFwxQ/0/Vcu8L/5RFYalHhSFyu/1vMTv3PpAr/kLMj0MTXQGPpgQwRdEALEEPThw4dzxhlnAPDNN98EZWx/Bb27HvrFF1/Mxo0b+eqrr/D0Cu4SI+/dyOjx5cAjuRx9uJDyDXqIpMQaQVYa/PEmEy/8r4m7l/ifjZKV1ry4yKjS+E2V/vy0qSbvjVVreFuP/XhHYuiCECCGdzx8+HBvtsmuXbuCMnZvCXp6ejqzZs3qli2jRo0C9IbSbrfbWyjM7dYomZxOvq2OM2fZ+HeulS8LUrggzUR0pIklZ3XrMmQkQ0l4S6X+rDgSzDBvKqQkmHjqDY20IZ3XhTkeEUEXhADx9dCjovTmx7t37w7K2F0JuhFD727IxQifdIfY2FhSU1MpKSnh6NGjZGZmUl1dTVxcHHdVjSLXBV9ebuKzFzSchbqn3RNsVhOu5AjwmdJhdwTjsiE9yUTqEI27L4eJI0XMWyMhF0EIAE3TvGKamZnJCSfoHe53794dlDIAvemh9wTDS8/NzUXTNEaNGkV6ejqnTtAX/3yyBQ4X68dmdZ4F2Sm2zGYP3WUNozosnHlT9Odms4nfXW/m0u+LoLdGBF0QAqC0tBS73U58fDyxsbEkJSWRlpZGXV1dUNrD+euhFxQU4HK5Oh3L7Xb7vSq0I4wSvrm5uRQVFVFWVobdbmfhLD3G/ckWzbvMv6ceOkDS8HDsJl2eqmMiwGRi1kQR8K4QQReEAPCNnxv4eumB0pWgR0REkJKSgtPp9Ip1R5SVleFyuUhMTMRm61mrNUPQDx48yPbt2wGYPHkyZ0zTxfazb/XFRNZwSO15EyWy0k0c89wYLTDp3royrufjHS+IoAtCAPjGzw0mTJgABH5jtKamhpqaGiIjI4mP73gFjb+56Ea4xZ8FSB3hG3IxBH3SpEmkJZoYl+nArndzY3iq/2mK7ZGVavKmLua5I4iKgPFZPR7uuEEEXRACwBBRQ1QheB56V6tEDfyNowcaP4eWIZdt27YBuqADzJ7Y1GxTAPFz0MM1RZ5Ml2JrJCeNAUsPW80dT0iWiyAEQG966F2FWwz6Q9APHjyI0+kE9JALwOwTG1m5Tm8YGsgNUdA/EN5KysJlMvFJwlCulHCLX4igC0IAtBdD9xV0TdN6nCvdXUHvKnUxGIKenp5OZGQkZWVlVFXpqzgnTpwIwMwTmjCbwe0O7IYo6OcX2KL569DxACjjxTv3Bwm5CEIAtBdySUtLIyEhgcrKSoqLi/0e65133uHaa68lLy8PTdP48MMPgeYaKh3R3Rh6IIJuMpm8cXSn08mIESOIi4sDID5aY9pY/bistMAEOCUBbNbm53JD1D9E0AUhANoLuZhMph6FXR544AFWrlzJ5MmTWbRoEatWrcJisXD++ed3el5nIZetW7cyZswY/va3vwVF0KE57ALN4RaDX19mYvYkOOfUgC6B2Wzy1myJjYKxwzs/XtARQReEHuJyudoNuUBz2KU7N0Zzc3MBPbtl3bp1REVF8fbbbzN3buddIToKuTgcDq6++moOHDjAvffe6xX8QAXd8NCh+YaowfnzTGxYbiY9KfAQiRGHnz4usIyZ4wkRdEHoIUePHsXhcJCWluZd8m8wfrwe+92zZ49fY1VWVlJRUUFUVBTPPfccCxcu5JNPPmHBggVdnpuRkeG1x3dx0aOPPsq33+pdIQoLC9mwYQMQXA+9taAHEyMOL+EW/+nypqiiKGnAGsABuIDLgNHAHwE3cJOqqtsVRUkHngeigadVVX2x16wWhBDAqDqYnZ3dZt+YMWMA2L9/v19jffed3gVi1KhRXHPNNVxzzTV+22G1WklLS6O4uJjCwkIyMzM5ePAg999/PwA//OEPWbt2LZqmVy4MJA/dsNGgdcglmFx2pomv9mhcuUC8c3/xx0MvBeaoqjoPXbCvBX4HLAZ+AvzBc9yd6CI/D7hZUZSB36BPEDohmIJ+8OBBoKVYdofWpW2fffZZGhoauOSSS/j73/9ORIT+72g2m0lOTu7RNQwMD91qtXrn2RucOcPEzufNTBwlgu4vXQq6qqouVVWNKkOxQC7gUlW1QlXVw0CiZ9/JwCeqqjoBFZjYGwYLQnc4cOAAGRkZPProo0Ef2/CqR44c2WbfqFGjMJvN5OXl0dTU1GZ/awIV9HHj9LiEEeLZuXMnABdccAGJiYlccsklgF7DJSwsrEfXMBgzZgzXXXcd9913H+Hh4QGNJQQXv2LoiqJMVRTlC2AZsAmo9tntVBTFCoT7CH8VzUIvCP3Ge++9x9GjR/nVr37Fjh07uj6hG3TmodtsNrKysnC73V7h74xABb11Vo0h7EYsf+nSpZhMJu8q1kAwm808++yz3H333QGPJQQXvxYWqar6DXCKoigXAf8DxPmOoapqk6IoDkVRzB5RjwfKW4+jKMoNwA0Ay5Yt48wzzwzU/l7F4XBQUFDQ32YEheN1Llu3bgX0nOkrr7ySN99809uYIVD27t0LQExMTLv2ZGVlkZeXx+bNm4mJiWl3DGMuRjZMfHx8j14no3ri1q1bOXjwIAcPHsRsNhMVFUVBQQHDhg1j7dq1pKen99r74Hh9j/U1xk3w9vDnpqhVVVXjO2MVUAtYFEVJQA/BGML9FXCaoiifAdOBX7UeS1XVFcAKz1PNT/v7jYKCgk7/eAOJ43UuRj0Uk8nEli1bePvtt1m6dGlQ7DDGVhSlXXsmTpzIZ599Rnl5eYf2GnMxxGPGjBk9ep2M1MaDBw9it9txuVzk5OS08Ph7+/U/Xt9joYQ/rspURVE+UxTlU+A24BHgf4H3gFeAX3uO+4Pn98+AZ1RVbQi+uYLQPYybkg888AAATz75ZFDGdTqdHD58GIARI0a0e4y/N0ZdLlen4Rt/GDVqFFarlcOHD/PVV18BzeEW4fihSw9dVdUvge+12lwInNrquEIgtGMownFFU1MTeXl5mM1mbr31Vu6//3727duH3W73Zn30FKOhxNChQzscy19Bz8/Px+l0MmzYMCIje9b12GKxMHbsWHbs2MGbb74JiKAfj8jCImHQYjQzzsrKIi4ujpycHNxuN/v27Qt4bH886rFj9cImHQm6y+WisbEx4BuiBsaN0XXr1gEi6McjIujCoMUQUsNTPvHEE4HmlL5A6Cxl0SA7O5uwsDAOHz6M3W5vsS8/P58JEyYwZ84cPvnkEyBwQTcyWIxriaAff4igC4OW1oJueLDBEHR/PPTw8HBGjhyJpmleLxygpKSE73//++zbt4/CwkIefPBBIHgeuoEI+vGHCLowaOlND93fm5it4+gul4tFixaxd+9eJk2a1OL8YHnoAMnJySQlJQU0njDwEEEXBi1GrLy/Qi6+1zZs2bVrF19//TWpqal8+OGHrFy5ktjYWKB5tWdPGTt2rDfHPhgLiISBhwi60O/s3r2bxx9/vEWlwGDQ2kMfN24cYWFh5Obmtolpd5eeeuhG6GX69OmkpaUxduxY1q9fzzPPPMOMGTMCsslms3nrrEi45fhEWtAJ/UpZWRnz58+nsLCQUaNGcc455wRlXLvdzpEjRwgLC/N60TabjZycHPbu3cvevXuZMmVKt8asra3l3Xff5csvvyQ/Px+TydSmDnprjEwXY1Vpexkt06ZNY9q0ad2ypSMmTJjA/v37A/b2hYGJCLrQb2iaxvXXX+9dcblt27agCXpubi6apjFy5MgWBaQmTJjA3r172blzp9+CXlVVxbJly3j99ddpaGheLzd16lRsNlun5xqesiHoRhOLQOPlHXH77bfjcDhYsmRJr4wvhDYi6EK/8dxzz7FmzRrv8+60a+uK1uEWgxNPPJE1a9Z0K47+xhtv8OKLenn/2bNns2DBAqZMmcK8efO6PDczM5PIyEiKi4uprKz0eui+TSKCybx58/yySxicSAxd6Bc0TePee+8F8NZW6U67tq4wPOL2BB26d2PUsOvee+9lw4YN/O///i/nnHOOtzlyZ5jN5hZhl2AtIhKE9hBBF/qFnTt3UlhYyNChQ7152Hv27MHtdndxpn988803AG3CKoagd+fbgJGh0jrP21+MsMvu3bv9zo4RhJ4ggi70Cx9//DEAZ5xxBkOGDGHo0KE0NDRw6NChoIxvlM2dOnVqi+1jx471ZrrU19f7NZbh7ff0RqNx3ieffEJTUxNpaWkdltMVhEAQQRf6BUPQ58+fDzTnTQcjjl5XV8e+ffuwWCxej9zAZrMxfvx43G63X2EXp9PpvZHZ03ZrhqAbNVYk3CL0FiLoQp/jdDr5z3/+AzQLuhHOCEYcffv27WiaxoQJE9rNQjHCMN9++22XY+Xl5eFwOMjMzCQ6OrpH9hiCfuzYMUAEXeg9RNCFPkdVVaqrq8nJyfE2Nw6mh26EW0466aR293dH0I34eSB53a3P7a0MF0EQQRf6nNbhFgiuh27cEG0dPzfojqAHGj8HvUWdb/cb8dCF3kIEXegUh8MR9DHbE3RfD13TAutO2NENUQND0Ldt29bhtRobG4FmQTdSD3uK7weCCLrQW4igCx2yb98+0tLSWLZsWdDGLC0tZePGjZhMJk4//XTv9tTUVBITE6murubo0aM9Ht/pdLJ9+3agY0FPT08nNTWVqqqqdrNqVq9eTWRkJMuXLw9KyAVa1laRkIvQW4igCx3y0EMPUVFRwVtvvRW0MVetWkVTUxMLFy4kOTnZu91kMnm99EDCLnv37sVut5OdnU1CQkKHx02ePBloG3bRNI37778fTdO44447vN5+oIJunB8REUF6enpAYwlCR4igC+1y+PBhXnrpJUDvrlNZWdmt848cOcJZZ53Fj3/8Y37zm99428GtWLECgBtvvLHNOT1pQLFnzx7Wr1/vfW7Ezzu6IWrQURz9o48+8t6YbWhooLKyEpvN5r1521MMQR85cqS3xK0gBBt5Zwnt8qc//Qmn0+l93t0a4q+++ioffvghr7/+Ovfddx+nnHIKf/3rX9m/fz+ZmZksXLiwzTlGiOTrr7/2+zoXXHABp59+OmvXrgXwtnPrKNxi0JGgP/bYYwDccsstXg8/JyeHsLAwv21qj3nz5nHBBRdw1113BTSOIHSKpmn99RPy5Ofn97cJQaM7cykuLtYiIiI0QJsxY4YGaM8880y3rrd06VIN0C699FJt3rx5GuD9eeCBB9o956uvvtIAbdy4cX7Nxel0ahaLRQO05ORkbfny5RqghYWFaVu2bOl0jG+//VYDtNGjR3u37d27VwO0iIgI7dixY9pzzz2nAdrVV1/drbl3h+P1PRbqhPhcOtRV8dCFNjz55JPY7XbOOeccLrroIgB27NjRrTGMmiUXX3wx7777LqeccgoAYWFhXHvtte2eM3nyZGw2G3v37vUrxFNSUuL9FlFaWsrNN98MwCOPPNJlyGX8+PGEh4eTm5tLTU0NAM888wwAl19+OcnJyVxzzTV89dVXPProo11PWBBCABF0oQW1tbUsX74cgDvvvJOJEycCPRf0kSNHEh0dzbvvvsvixYu59957W+Rk+2K1Wr1CrKpql9coKCgAYMSIEd7+mRdffDG33XZbl+darVbvjVHjWkYs/rLLLvMepygK8fHxXY4nCKGACLrQgpUrV1JRUcGsWbOYPXu2V9CN5fT+oGlamxZtSUlJvPPOO96SuR1x8sknA/Dll192eZ38/HwAJk2axIcffsjvfvc7nnvuOUwmk192zpw5E4AvvviChoYGtm3bhtlsRlEUv84XhFBDBF3w4nQ6veGFO+64A4CMjAzi4+MpKyujuLjYr3GKioqw2+0kJib6VTPcl54IemZmJieddBJ33313t+qtGIK+efNmtm7disvlYuLEiT2u2SII/Y0IuuBlzZo15OXlMWbMGH74wx8Cen54d8MugdT8NgT9iy++6PIbga+g9wRfQf/iiy9aXF8QBiIi6IKXTz/9FIBrr722RZpedwXdCLf0RNBzcnJISEigqKjIK9gdEaigjx49mqSkJIqLi1m9ejUggi4MbETQBS/GCs3WXX760kM3mUx+h10CFXSTyeT10j///HNABF0Y2IigC16MFZLGEnyDvhR0wJvi2NuCDs1hF4CoqKg2DTEEYSAhgi4AUFZWRklJCdHR0QwfPrzFPkPQt23bht1u73KsQAXdSF3srLytpmleQe8oDdIffAV9+vTpWCyWHo8lCP1Nl+9eRVFOBh4HHEABcAVwHnA70ABcqapqvqIo44EVnjHvUVX1494yWgg+RrjlhBNOaFNrJDk5mWnTprFlyxY+/vhjFi9e3OlYgQp6R8vy6+rqWLVqFRaLhQsuuIDGxkYSEhIC6s85Y8YMTCYTmqZJuEUY8PjjoR8BzlBV9XtAHnAu8HPgNOBe4B7PcQ8B1wILgN8E21Chd+ko3GJgZL0YNVM6wul0cvjwYUBf8NMTsrOziY2NpaioiJKSEgD+8Y9/kJOTw7Jly7jpppu8WSmBhFsA4uPjvUXBZsyYEdBYgtDfdCnoqqoWqqra4HnaBIwDdquq2qSq6kZgsmffMFVV96uqWg2UK4qS3N54QmA8/vjjnHvuuZx77rn8/Oc/b1FAKxAMD90Qt9YYgv7222/jdrs7HCc/Px+Xy8WwYcOIiIjokS1ms7lFedtt27Zx1VVXUVRUhM1mQ9M0/vSnPwGBCzroZYKvuuoq7xwFYaDid8BQUZQRwFnAXUCKzy4jv833w6EKSARKW41xA3ADwLJlyzjzzDN7YHLf4XA4vMvLQ4HS0tI2y9onTJjQbuXC1nQ1F6Pud2pqarvHpaSkMHToUAoLC3n//fdbVDN0Op1s2rSJjRs3es/NyMgI6G+Xk5PDxo0b+eyzz7z56Oeeey4LFy7kxhtv9C7THzJkSMCv0fTp05k+fTrl5eUBjdMTQu09Fggyl76hs3tGfgm6oihxwAvAVegC7rv8z+V59HXb4oE2/x2qqq5Aj7ODXnkvpCkoKAjohluw+e9//wvoqXUzZsxg+fLlrF27luuuu67Lc33nsnv3bmpra1uEGA4ePAjA3LlzO5zzj370I/7yl7+wefNmbxz9jTfeYOnSpW1WkU6ZMiWgv92pp57KP/7xD/Ly8jh27Big12n50Y9+xK9+9Suqq6sBvTVcKL1G3SXU3mOBIHPpf7oMuSiKYgFeAR5QVXUvsB84QVEUq6IopwLbPIcWKooyWlGUWCBRVdXSDoYUeojRi/P888/n3nvvxWKx8N5771FUVOT3GPX19cydO5dTTz2V/fv3A1BTU8ORI0ewWq2d3sg899xzgZZx9N/97ncUFxeTk5PDXXfdxSOPPMLjjz/Ogw8+2JMpejFCLl999RWfffYZAKeffjoREREtvpEEI+QiCIMFfzz0S4FTgHsURbkHeBp4DFgP2IErPcf9D7AK3YO/L8h2CrRsrpyamsqiRYtYu3YtL774Ir/85S/9GuOVV16hrKwM0MvMrlixgj179gB6V53O0vbmzZtHbGws27ZtIy8vj4SEBLZu3YrVauXbb78lKioqwBk2M2nSJEwmk7dJ84knnuht3Xb++efz6quvAiLogtCCzoql9/JPyBNKRe4PHjyoAVpCQoLmdDo1TdO0NWvWaIA2YcIEze12d3q+MRdFUbyNJqxWq1ZQUKCtWrVKA7SLLrqoSzsuuOACDdCeeuopbe3atRqgzZ07N/AJtsOYMWO8tt5yyy3e7YcOHdKGDx+uAdr+/ft75dp9RSi9xwJF5tJnSIOLgY7hnZ9++uneOiuLFy8mJSWFXbt2+VU/XFVVVFVlyJAhnHPOOTQ1NfHQQw95x+4ow8WXc845B9CzXYwbk/PmzevJlLrEtwTB/Pnzvb+HhYXx7rvvsmbNGnJycnrl2oIwEBFBHyD4hlsMwsPDufDCCwFYt25dl2MYHXmuuuoqHnjgAQCWL1/OCy+8AHTdhxNg0aJFmEwmPv30U9577z0ATjvtNL/n0R0MQTebzW0+NCZNmsR5553XK9cVhIGKCHoIcejQIZYtW0ZhYWGL7U6n09v82FfQAc444wygudtORxw8eJAXX3wRgJ/+9KecdNJJXHDBBYBeO+WZZ57h7LPP7tLGlJQUZs6cSVNTE3v27CE8PJxZs2b5Nb/uYjSamDlzpnQNEgR/6Cwe08s/IU9fx9GWLVumAdo111zj3eZyubQlS5ZogDZy5Mg2sfKSkhJvY2O73d7uuG63W5s9e7YGaFdccYV3e2Njo1ZSUtJtOx966CFvbHvOnDndPt9f3G639vTTT2u7du1qsT3E45vdQuYSmoT4XCSGPhAwFve89tpr1NXVoWkaN998My+++CLR0dH885//bNNeLSUlhYkTJ2K32zusTvj888+zceNGkpKS+POf/+zdbrVaSUlJafeczvD15Hsr3AJ6edsbb7yxw3IEgiC0RErLBYnDhw/z+9//noaGBiwWCzfffHOXned9cbvdbNump/TX1tby+uuvA3rc22az8fbbb7eoDOjLaaedxo4dO1i/fj1z585tse+bb77h5z//OQD/93//R3Jy4BUZJk6cSHZ2Nnl5eZx++ukBjycIQpDozH3v5Z+Qpztfu2666SZvGALQpkyZ0mUqoS9GWqLxM2PGDC0lJUUDtJUrV3Z67r/+9S8N0M4444wW2//9739rMTExGqB9//vf75Y9XbFx40btscceC+qY/hLiX4e7hcwlNAnxuXSoqyLondCdF9XImX7wwQe19PR0DdDee+89v883cspnzpypRUZGeoV97ty5XYpme3H0HTt2aBaLRQO0n/zkJ1pubq7ftoQ6If7P1i1kLqFJiM9FYui9yeHDh9m/fz9xcXHceeed3hDHQw895PcYRu3vuXPnerNPLBYLTz/9dJu4eWvai6OvXr0ap9PJRRddxAsvvIDNZuvJ1ARBGECIoAcBI0d83rx5WCwWbrzxRhISEtiwYQMbNmzwawxD0KdMmcLtt99ObGwsv/nNb/xuiWbEst99910APvjgAwCWLFnSpmGFIAiDE/lPDwKtF/3Exsbys5/9DNDrpfiDIeiTJ09m2rRpVFVV8etf/9pvGwyv/pVXXqG8vJwvvvgCi8XSq1kogiCEFiLoAaJpWrurOG+88UYAPvnkE1wuV7vnGlRXV3Pw4EGsVivjx48H6DLM0pq5c+cyfPhwDh06xG9/+1vcbjezZ88mNja2W+MIgjBwEUEPkN27d1NUVERaWlqL8MiwYcPIzs6mtraWnTt3djrG9u3bAb2WSnh4eI/sMJvNXHrppQA88cQTAJx11lk9GksQhIHJcSHohYWFbNy4kY0bN3LkyJGgjm1452eccUYbr9pYEr958+ZOx3jrrbeAlsWoesJll10G4G0R94Mf/CCg8QRBGFgMekGvqKjgxBNPZM6cOcyZM4fx48dz6NChoI1v1FBpXWMF8C4E6kjQ3W43t99+uzfOfv755wdky+TJk5k4cSIASUlJ3VrYJAjCwGfQC/rf//53KioqSE9PJysri/r6+hbL3wNB0zRvW7jvfe97bfYbgv7555+32We327n44ot57LHHCA8P58UXXwxKk+IlS5YAsGDBAsluEYTjjc6S1Hv5p9dxuVzaqFGjNEB76623tG3btnkX4BQXF3d5vu/igpdffll78803W+zfs2ePBmhpaWntLv5pbGzUbDabBmjl5eXe7aWlpd5iWXFxcdrHH38cwCzbXvPJJ5/Ujh492uFcBjoyl9BE5tJnHJ8Li95//30OHjxIdnY2ixcvZtKkSZx99tnY7XYef/xxv8fJzc3l0ksv5bzzzmPlypXe7UaO+Zw5c9rNSrFarUyfPh2gReGsK664go0bN5KZmcnGjRu9JXCDgdVqZdmyZQwdOjRoYwqCMDAY1IL+1FNPAbB06VJvl5+7774b0Bs7VFVV+TWO0b8S4LrrrvM+N8ItrQti+dI67NLU1OS9kbphwwZvzFsQBCFQBoWg2+32NtsOHDjAunXriIiI4JprrvFunzVrFnPnzqWqqop//etffo1vCPjChQvRNI3LL7+cvXv3dkvQjRuj27Zto7GxkXHjxjFixAj/JigIguAHA17Qn3rqKSIjIznvvPNa5HuvWLECgEsuuYSkpKQW51x++eUA3hK1nbFr1y62bdtGQkICb775JldeeSUOh4Prr7+egwcPEhMTw+TJkzs830hd3LRpEw6Hwxt6Ofnkk7s3UUEQhC4Y0ILudru9GStvvfUWkyZN4q9//SuNjY3eWLexYtOXc889F7PZzEcffURlZWWn1zC88/PPPx+r1cpDDz1EVFSU1zs/9dRTsVg6LiufmZnJhAkTqKmp4b///a8IuiAIvcaAFvQNGzaQl5dHZmYmS5cuRdM0br31Vn77299SVlbG1KlT2xXO1NRU5s6di8Ph8Bazag9N03jllVcA3dMHfQXoL37xC+8xnYVbDM455xwA3nnnHRF0QRB6jQEt6M8//zygZ40sX76ca6+9lsbGRn73u98BunfeUU0Uo5hVZ2GXf//73+zbt4+hQ4e26Mxzxx13kJqaCtCmG317GC3bVq9ezZ49e7BarQGvChUEQWhDZzmNvfwTEPX19VpsbKwGaLt379Y0TdOqqqq07OxsDdBiYmK06urqDs/Pz8/XAC0yMlKrra1ts9/lcmnjxo3TAO3JJ59ss3/Lli3as88+61fHHofDoSUmJnqbVpx88sndmGlwCPG82m4hcwlNZC59xuDLQ3/11Vepqanh5JNP9lYojIuL4/nnnycmJobbbrut00qDGRkZzJw5k4aGBm/YpaSkhIsvvpg//OEPrFq1ir1795KVlcX111/f5vyTTjqJ6667zq+qiBaLhUWLFnmfS7hFEITeYEA2id6wYQM333wzANdee22LfXPnzqWystKbd94Zl1xyCZs3b+Yf//gHF110EX/+85957bXXeO2117zH3HvvvUHp9nP22Wfz4osvAiLogiD0DgPOQ9+yZQuLFy+mvr6eq666iuuuu67NMf6IOcBPfvITLBYL69at49ChQ6xatQrA6/GPGjWKK664Iih2/+AHP/Bmw4igC4LQGwwoD93okVldXc2FF17I3/72t4AKUKWkpHDOOeewZs0aLrvsMkpKSpgwYQLbt29n06ZNREVF9bg+eWsSEhJ46qmnKCoqYuzYsUEZUxAEwZcBJegWi4XXXnuNRx99lOeee85vT7wzrr76atasWcPGjRsBuP766zGbzcyZM4eCgoKAx/flpz/9aVDHEwRB8KVLQVcUJR74EJgAzFRVdYeiKBcCtwMNwJWqquYrijIeWOEZ8x5VVT/uDYOnTZvGCy+8ELTxFi5cSFpaGsXFxVitVu8qUkEQhIGGP/GKemAx8C8ARVEswM+B04B7gXs8xz0EXAssAH4TbEN7C4vF4o2Tn3/++W3KBAiCIAwUuvTQVVV1AMcURTE2jQF2q6raBGxUFOVPnu3DVFXdD6AoSrmiKMmqqpb2htHB5p577iExMbFFES9BEISBRk9i6EOAap/nRiDb19uvAhKBFoKuKMoNwA0Ay5Yt48wzz+zB5XuHyy+/HIfD0SJu3vr5QEbmEprIXEKTUJ5LRkZGh/t6IuiVQJzPc5fn0e2zLR4ob32iqqor0OPsoK+aDGkKCgo6/eMNJGQuoYnMJTQZqHPpiaDvB05QFMUKKMA2z/ZCRVFGAyVA4kAJtwiCIAwW/BJ0RVHeA6YC44C/Ao8B6wE7cKXnsP8BVqGHYO4LqpWCIAhCl/gl6KqqLmpn86utjtkFdF1LVhAEQegVBtzSf0EQBKF9RNAFQRAGCSLogiAIgwSTpoV89qAgCILgB+KhC4IgDBJE0AVBEAYJIuiCIAiDBBF0QRCEQYIIuiAIwiBBBF0QBGGQIIIuCIIwSBBBBxRFifY8mvrblkBRFCXK8zgY5jLC8zgY5nLKYJgHgKIoWf1tQ7BQFGVIf9sQTI7rhUWKopwFXA8cBf6gqurRfjapxyiKch6wBDgCPDLA5xIF/BEYDvzY0zVrQKIoyhTgcWAzcK+n09eARFGUBcAyoBF4GVinqmpt/1rVMxRFmQf8Ar0Jz3Jgp6qq9v61KnCOdw/9J8DfgB3AjYqiDMhqkYqinA1cDfwBvQHJnZ7tA9IjVFW1HmgCYtHnNWDngl6B9CFVVe8CRvW3MT1FUZQw4Eb0BjUPoPdCiB7Ar8vFwN/RP5gWARf0rznBoScNLgYsHs/vYmADUAwcBr4EPvVsn64oSu5A8G49c7kUeB/YAlynquoxRVH2Aa8oipKqqmpJvxrpJz6vy2eqquZ6ROIA8AZwi6Io61RVPdyvRvqJ73vM02O3HligKMpd6E1gvgLeVlU1tz/t9AfPXC4B/gPUAtvRv80eQu+PEAmEo3/4hjSKokSiN7Vfp6rqf4DvgEL0/387sFhRlPGqqu7pRzMD5rjx0BVFuRS9KUcUcFBV1WogHZjl+Rq8FYhAb58X0vjMJQIoUVX1qEfMzehe7XcDSMyNuUSif8CiqqoGTEB/Ld4AfqooyvD+stFfWs0lz7M5ChgK/BJYih6uWNwP5nWL1nNRVbUY+Bg9rLcVPVRxPXBzf9noL573zsvoTtznns0mYCR6K8xd6O+9nH4xMIgcF4KuKEoccBHwW/Q35fcVRUkGngauUxQlWlXVHcAIILvfDPWDduZymqIo4wFUVXWjC4jTc2xWKH8lbjWXT4B5iqKc6Nn9H/RvHnXoInKL55yQfM+2M5fTFUUZBryO7sUOV1W1Cl3ojdcnJF+bdt5j8xVFGaOq6nrgI2C5qqpLgHcAq6Io5lCdiwcLsBb9m/jPFEU5Ffg3cCpwoqqqZejOUSSE7uviD4P2pqjnTvwvgXeBjcD3gNsBK/A2cAUwD7gB/QX/L3q89nVVVd/pD5s7oou5rEWfy7mqquYpinIt+hu1CkgCbg6lG1d+zuUs4KfAaeg9ao8Cdaqq3tMPJneIn++x+ejzmIzuCS4CDqiq+kA/mNwhfr4uC9G/XQxFF8RlQIWqqrf0h80d4TOXtej3xzI9zwvQnYSrgIeBSegN7/cAZ6OH/P7WDyYHjZD0dgJFUZRM4M/osb504HlVVd8DHgFOV1X1T8DzwB9VVf0D+hv4p8C2EBTzrubyZ/SbO3/wnJKFLuj7VVW9MsTE3J+5PA/cD/wJWKmq6iWqqv48BMXcn/fYP9Czp1ajf+U/BdgUgmLenddlJXqj+PuBL0NQzH3nkgH8RVVVFd25aVJV9SXP/rOAF9BDevOArwa6mMMgE3RFUb7n83UpQVXVP6uq+g8gVlGUX6uq+gF67Az0RtdRiqLEer5KXqmq6qN9b3X7dHMuT+H5Go/+lXiWqqpP97HJHdLNuTyO7jWhquqLnvND5n3ag7lYFUWJ8/Tc/cUAf12igQhVVV9G/0b4ZD+Y3S6dzCVeUZTrgN8BJwOoqroOGO85bgdwSyjNJRBC5h8lEBRFiVEU5UP0eN8i9Bs2GxRF+annkP8CP1QUJUFVVZeiKN8D3kTPpKgFUFXV2XbkvieAuRwEUFX1v6qqVva95W0J5HXxpC4C3nsD/UoAc8n13IBHVVVXP5jehgBflzqAUMmn92MunwHXeB43KIpyn+f4o55jQ+Z1CQaDJoauKMp09IUoJ6MvFEjwPOahi3Yduve6E3gW/ev86/1ha1fIXGQuvc1xNpdG9A+kz4E09BuhH/SDqb3OoBF0A0VRnkCP7b2oKMpQ9K/vB4DbgJdUVS3qT/u6g8wlNJG5hCZdzOWFgZLKGwiDIuQCLVKNXkJPGUtVVbUQPZd5NXpKYk0oxWM7QuYSmshcQhM/51I7kNMR/WXQeegAiqL8DBgNVAC5wD5VVb/sX6t6hswlNJG5hCaDaS49IeQ/fbuDjzcxGT1n9qCqqi8OxBdU5hKayFxCk8E0l0AYrB76BcA7qqo29rctgSJzCU1kLqHJYJpLTxiUgi4IgnA8MqhCLoIgCMczIuiCIAiDBBF0QRCEQYIIuiAIwiBBBF0QBGGQcFy1oBOOTxS9ldqv0DvvrFIU5Sr0ksN3eMrcCsKgQDx04XggCrgPvbEB6N2QLkVvQiEIgwbx0IXjAdXzOE9RFA29+cEI4A5gr6IoeUAyekOKJehNxJ9C73BvAa5WVXWdoihW4CH0D4No4ENgqaqqx/pwLoLQIeKhC8cDd3sed6OLcXthlmjP4+fodbWfRu/Yk4rergzg18Av0D37x9CXmD/TKxYLQg8QQReOB4za1yWqqr6Cp6lJK9zoPTSNmt8vqKr6BHojhJGebWd7Hn+KHsKJBs7sFYsFoQdIyEU4HvCnvkWDqqpNiqI4PM+rPI8uIMznOCe6sBtdbsQpEkIGeTMKxwPV6B54jqIol6HHz3vCO+hO0JXozbgXoHvrghASiKALgx5VVR3o8fAE4EWavevu8nvPOHPRb5ouRM+YEYSQQKotCoIgDBLEQxcEQRgkiKALgiAMEkTQBUEQBgki6IIgCIMEEXRBEIRBggi6IAjCIEEEXRAEYZAggi4IgjBI+H+zoeGM92v/bgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(label='train')\n",
    "val.plot(label='true')\n",
    "pred_best_theta.plot(label='prediction')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the model with `best_theta` is so far the best we have, in terms of MAPE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting: simulate historical forecasting\n",
    "So at this point we have a model that performs well on our validation set, and that's good. But, how can we know the performance we *would have obtained* if we *had been using this model* historically? \n",
    "\n",
    "Backtesting simulates predictions that would have been obtained historically with a given model. It can take a while to produce, since the model is re-fit every time the simulated prediction time advances.\n",
    "\n",
    "Such simulated forecasts are always defined with respect to a *forecast horizon*, which is the number of time steps that separate the prediction time from the forecast time. In the example below, we simulate forecasts done for 3 months in the future (compared to prediction time).\n",
    "\n",
    "Using the `backtest()` method, you can either look at the performance of the model evaluated over the whole forecasts it produces (each point in each forecast is used to compute an error score) or only the last point of each historical forecast.\n",
    "In the latter case, you can get a time series representation of those points by calling `historical_forecasts()` with the default setting (`last_points_only=True`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b461f8ca6bca4d14a48e026a519e4dd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84366325059340bfa0d3780f68990c74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average error (MAPE) over all historical forecasts: 6.133842206716315\n",
      "Median error (MAPE) over all historical forecasts: 4.205415091459073\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56fa658ebc4e409b8e949330df09e909",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd59cc55af42428188a173712ed25a09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_theta_model = Theta(best_theta)\n",
    "\n",
    "average_error = best_theta_model.backtest(series, start=pd.Timestamp('19550101'), forecast_horizon=3, verbose=True)\n",
    "median_error = best_theta_model.backtest(series, start=pd.Timestamp('19550101'), forecast_horizon=3, reduction=np.median, verbose=True)\n",
    "print(\"Average error (MAPE) over all historical forecasts: {}\".format(average_error))\n",
    "print(\"Median error (MAPE) over all historical forecasts: {}\".format(median_error))\n",
    "\n",
    "raw_errors = best_theta_model.backtest(series, start=pd.Timestamp('19550101'), forecast_horizon=3, reduction=None, verbose=True)\n",
    "plt.hist(raw_errors, bins=np.arange(0, max(raw_errors), 1))\n",
    "plt.title(\"Individual error scores (histogram)\")\n",
    "plt.show()\n",
    "\n",
    "historical_fcast_theta = best_theta_model.historical_forecasts(series, start=pd.Timestamp('19550101'), forecast_horizon=3, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what this backtest forecast looks like. You can see it produces more accurate predictions at a 3 months horizon than the one-off prediction done above, because here the model is re-fit every month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series.plot(label='data')\n",
    "historical_fcast_theta.plot(label='backtest 3-months ahead forecast (Theta)')\n",
    "plt.title('MAPE = {:.2f}%'.format(mape(historical_fcast_theta, series)))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the fitted value residuals of our current `Theta` model, i.e. the difference between the 1-step forecasts at every point in time obtained by fitting the model on all previous points, and the actual observed values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_residuals_analysis(best_theta_model.residuals(series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the distribution has a mean that is slightly larger than 0. This means that our `Theta` model is biased. We can also make out a large ACF value at lag equal to 12, which indicates that the residuals contain information that was not used by the model.\n",
    "\n",
    "Could we maybe do better with a simple `ExponentialSmoothing` model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b2f3dc9a7874ab18eeac3e426df381d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/70 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_es = ExponentialSmoothing()\n",
    "historical_fcast_es = model_es.historical_forecasts(series, start=pd.Timestamp('19550101'), forecast_horizon=3, verbose=True)\n",
    "\n",
    "series.plot(label='data')\n",
    "historical_fcast_es.plot(label='backtest 3-months ahead forecast (Exp. Smoothing)')\n",
    "plt.title('MAPE = {:.2f}%'.format(mape(historical_fcast_es, series)))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This much better! We get a mean absolute percentage error of about 4-5% when backtesting with a 3-months forecast horizon in this case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8WTJkiW0adOG1q1bu21KVHC7m78gCPWUHTt2cOGFF/pqlSXLg6yuCyStte9zsrXP7uLfo0cPX6028SDFh9zcXA455BBOP/10t02JGiKQBEFwhSuuuIL169f7vieLQHKG2DZv3lzrYrSJxpw5c3yfk619gTxIIpBqRmFhIUcddRTPPPNMWMvPmTOH0tJSVq5cGWPL4ocIJEEQ4s5bb73FW2+9RaNGjXzV5JNBIJWUlLBz505SUlJo2LChb7iRuoTTg5Rs7ZMQW/SYO3cuP//8M88991xYy9t5a9u2bUsqUR0KEUiCIMSVtWvXcvnlVv+Lxx9/nH79+gHJ8aZve49atmxJ27ZtgeQLQ4WiqKiIRYsW4fF4fO1Lpjwkp0CSEFvtKC4uBqzrNRzB89tvvwFWnaK6IkpFIAmCEFfuvfdeCgoKOPXUU7n44ot9D7JkuKnaAqlVq1Z1UiAtXLiQ8vJyDjroILp27QokT/t2797Nhg0bfONYSoitdhQVFQGwa9cuduzYUe3yzqGotm/fHiuz4ooIJEFIcsrKypLKpW3XqbniiiswDCOpBJKdoF1XBZKdf9S/f39fN/lk8SDZCdrdu3cnJSVFQmy1xPYgAb5xQYNRWFjIqlWrfN+3basbJQxFIAlCElNYWEi3bt0YNWqU26aEjf3Abd++PUBSPchsgdSyZcukExDhYOcfKaWSLsTm7MEGSIitlkQikBYvXlzpe10RSG4XihQEoRbMnDmT9evXV7qZJTqbNm0C9gmkZPIg1fUQm9ODtGXLFiB52ufMP4LkEt6JSCQCyRleg7ojkMSDJAhJzKxZswCrplAyhNmKiorYsWMHaWlptGzZEkiuN/26HGLbtWsXy5YtIy0tjT59+iSdh8xfIDmFd0VFhWt2JSt2DhJUL5DsBG3DsMZ9FYEkCILr2GNmlZaWVrqhJSr2w7Zdu3a+m2kyeZCcIba6JpDmzZuHaZr07t2bBg0aJF37/AVSSkoKGRkZmKaZFNdGouH0IK1ZsybksrZA6tOnDyBJ2oIguIxpmj4PEhBWTxO3cQokm2QSSIFCbMniYakOO/+of//+AEnlQTJNs4pAAgmz1YZwQ2ymafoE0jHHHAOIB0mop7z55pu8/fbbbpshYA3MaeeJABQUFLhoTXj45x9Bcj3EnCE2W0Aki4elOuz8I6UUQFJ5kDZt2kRhYSEtW7b0hW4hucK3iUa4Amnt2rXs3LmTtm3bcuCBBwJ1RyC5mqStlDoceAIoBXKBC4HTgH8BRcBYrfUG1wwUKlFcXMy4ceMwDIMzzzyT9PR0t02q1zi9R5AcHqRAAimZPEjhhtjWr19PeXk5Xbp0iad5tcLfg+T0kJmm6QuJJiJ//vknYHXxdyK1kGqOMyyZn5/P7t27fdeqE2d4rUWLFkDdEUhue5DWA8dqrY8C1gAjgWuAY4BbgVtcs0yowsaNGykrK6O0tJTc3Fy3zan32PlHNskukJLhIeYMsTVp0oSMjAz27NlTyfby8nIGDRrEgAEDKCkpccvUiNi2bRurVq0iIyODgw46CLCOS5MmTSgpKUl47+S6desA2G+//SpNTybvZKLh3zPW3sf+2D3YnAJJcpCigNZ6k9balqklQA9gqda6RGs9HejjnnWCPxs27HPmOQcZFWrO5s2bOfLII3nnnXci/q0tkJo1awYkh0AKlIPUsGFDwLohl5eXu2JXuDhDbIZhBMxD+uOPP9iwYQNbtmxJmheJuXPnAnDIIYeQlpbmm54seVb2/chfICWT+E40/AVSsDCb7UHq27cvzZs3B+qOBykh6iAppToDxwP/Blo7ZqUEWX4CMAFg4sSJDBs2LGa21QVvSbTasGjRIt/nhQsXkpOTU+t1hkNdPgafffYZ06dPxzRNhgwZEvb6ysrKfCGRQYMGMXXqVNasWROz/RStY7B69WoA0tPTK63PHvh15cqVvrf+aFPbNjgHqi0sLGT37t20aNGCtWvXsnjxYp/Q++qrr3y/mTdvXtRC0bG8Dr7++msADjzwwErbsD0CixYtokmTJrXeTqzasHTpUgCysrIqrT811XrErVu3Lirbrcv3In9sL1BmZia7d+9m4cKF9O7du8py8+bNA6yXHttjunXr1oS/F9lkZ2cHnee6QFJKZQGvA+OwBFGWY3bA10mt9QvAC96vMS3+kpubG3IHJgPRasOePXt8nwsLC+O2X0LZP23aND777DPGjx/PoYceGhd7akKwNtgPz/Xr10e0PxcuXEhRURHdunWjT58+TJ06FQh9sdeGaJ1D9k334IMPrrS+Jk2aUFRURNOmTSt5l6JJbdtge1FatmxJx44dAejUqRPz58+noqLCt247HwasPI5oHZNY3YveeecdnnrqKQBOOOGEStvo1KkTs2fPrtS+2hCrNtgei969e1daf6tWrQBo0KBBQtvvj2ma/PDDD8yaNYsrrrgiKuLUJtw22DlnPXr0YN68eRQUFFT53e7du1m9ejWpqakcffTRvrylQMtGi3g+k10NsSmlUoF3gDu01suBFUBPpVS6UmoQ8Jub9gmVcar2RAmx3XHHHTzzzDMcdthhDBs2jK+++iopCiba2LkRGzZsiKgatp2gffjhhydViM3OQfIXQcmQqO0Mr9kEStS2w1UQPG8jEaioqOC2225jzJgxFBcXM2HCBM4666xKyyRLV/9gOUjJFmKrqKjgo48+YsCAARx77LFMmjSJc88915VCl7bYsXumBQqxLVmyBNM06dmzJ+np6WRlZfk8rKWlpXG1Nxa4naQ9BhgA3KKU+gE4A3gc+AG42/snJAiJmIO0detWwPLEfPPNN5xwwgkopRL+hm5jCwLTNH3hp3Cw84+SSSCVl5f7yhLYwsImGR5kzh5sNv4Cqby8nPnz5/vmJ8p14s+ePXsYM2YMd955Jx6Ph8cff5znnnuOlJTKWQ3J0tXfFkidOnWqND2ZkrR/+OEHDjroIM4880zmzJlDq1ataNasGZ9//jl33nln3O2xX9jsulKBikU6E7TB8jrZeUh1IVHb1RCb1vp1rPCaP+/G2xahepwepER5M7YvwsWLF/Phhx/y6KOPMm/ePD7//HMuvvhil62rHueNe9WqVfTs2TOs39kCacCAAb6HcKILpK1bt1JRUUGrVq2q5OUkgwfJ2YPNxt/DsmzZskqh6ES5Tvy5++67ee+992jSpAnvvvsuJ510UsDlksGDVFhYyPbt22nQoAGtW7euNC+ZuvnffffdLF++nM6dO3Pdddcxfvx4fvnlF0466STuuOMODj30UE499dS42eMvkAJ5kJwJ2jbNmzcnPz+fbdu20aZNmzhYGjvc9iAJSUSieZAqKip8AqlLly78+9//5rLLLgOqHzsoUXDeuFeuXBn2b5YsWUJqaiqHHHJI0niQAnXxt0kGgRROiM1OnO/WrRuQuAJp5syZAEyePDmoOAKSohimswebf62mZPBM2tje1Y8//piJEyfSqFEjjj/+eO69914ALrjgAl+18HhgC6Tu3bvj8XjYuHFjlbIV/kOMAHWqFpIIJCEsysvLfQ+49PR0tm/f7vpNZ9euXVRUVNCkSRNf12S7MF91YwclCk5BEK5Amjt3LhUVFfTp04eGDRvStGlTIPEFUqAu/jbJEAoJJ8Rm5x+dfvrpQGK8SARixYoVAPTr1y/kcsnQzT9YeA2S47yysQWF8/wCuOGGGzjrrLPYuXMnp59+Ort27YqLPXYOUpMmTejQoQOmaVZ6STZNs0qIDahTtZBEIMWQF198kT59+iT0zSVc8vLyKC8vp02bNr4bkds3f/uGYl+QkNwCadWqVWH9xpmgDclTBynZPUiBQmzBPEjHH388jRo1oqCggJ07d8bZ0tAUFhaSm5tLWloanTt3DrlssnmQ/ImnB2nKlCm8/PLLNf59MIFkGAavvvoqvXr1YunSpTz33HO1sjNcbA9Sw4YNfeeJ0zO/cuVKCgoKKg27A+JBEsLkrbfeYtGiRfz8889um1Jr7DeHjh07+m5Ebgsk+w3FTgqE5BZI4XqQnAnaULcEktteyVAECrE5c3TKyspYsGABAIcddljCvEj4Y59nOTk5VZKy/bFzSPLy8lzpSRUOwXqwQfw8SKZpcsEFF3DxxRf7ajJFQlFREUVFRaSnp9OoUaMq8xs3bsw///lPwMq3DIf169czfPhw3xh7kWILpIyMDN991SmQPvroIwCGDRtWKbRZl4pFikCKIfYbZyK/fYWLnaCdnZ3tu/G7nV8RyIPUsWNHPB4Pubm5STHMg1MQrFmzhrKysmp/Y3uQBgwYAFQWSIlc4iDZPUiBQmzO4UbmzJlDUVERXbt2pWXLlr4HttvXiT92eO2AAw6odtmMjAyaNWtGWVlZwj7wwgmxxVp4b9q0yRf6mjJlSsS/d97Lgo15Z48zF66n+ZVXXmHq1Kk8+eSTEdsDlQVSIA/Se++9B8A555xT6XfiQRLCwhZIzhHXkxWnBylR3oztC9DpQUpLS6Njx46Ypum6feHgFARlZWXVPkznz5/Phg0baNKkia93SUZGBg0aNKCkpCSiWkrxJlQOUjIIpEAhNudwI3axTqUUQMK8SPjzxx9/AOEJJEj8MFsihNhs0Qm1F0jBsEcuCNfTvGTJkiq2hYtpmr4cpEACaeXKlcybN48mTZpwwgknVPqt5CAJ1WKaZp0SSE4PUqKF2PxvKskUZrMFgS3yQt38TNPkqquuAuCiiy6qFB5JhjBbKA9SMiTTBvIgwb48pC+++AKwwmtAwlwn/tgCaf/99w9r+URP1E6EJG3ndTtr1qyI91Ww/CMnHTt2pEGDBuTl5YUl+GyBZB/vSCgrK6OiooKUlJRKuWq2QHr//fcBGDlyJBkZGZV+Kx4kAYCnnnqKE088MeBb+549e9i7dy+QuG9ekRDIgxTozdi+sOJBsLeuZBRIdh2RUO7zt956i19++YXWrVtz2223VZqX7AIpGTxIgXKQYJ+HxS4QaXuQ6kKIDRLbg+T0FLsZYnN6aUzT5LPPPquyTEVFRdB7YzgeJI/HQ9euXYHqw2wlJSU+YbR9+3bfy3q4OMNrQBWBZIfXRo0aVeW3koMkAJZAmjZtWqWhBWycJ2Rd8iBVl6Q9fPhwunfv7hOHsSRQkjYkt0AK5kHatWsX119/PQD333+/TxDZJLpAMk0zoQXS77//HnLbpaWlvoFq7bIKNv5Vwe0xAetKiC2RPUhbt25l7969tGjRIuAgx/EOsR177LEAfPLJJ5Xmm6bJqFGj6NixY8BejfbzIpRAgvDzkFasWFEpnzFSL5K/QHKK/WXLlrFgwQKysrI4/vjjq/xWPEgCpmn6RIM93IWTuiaQbA+Sf5K2Myl4y5YtfPXVV6xevTouI17XBQ+SfeO264gEE0h33303mzZt4vDDD2fcuHFV5tsCqaCgICZ21padO3dSXFxMZmamqw+yQMyfP59evXr5iowGwvkA83gq3zadAiknJ8cn2BMxxLZt2zb++usvMjMzAwrVQCSyBylUeA3iH2L717/+hWEYfPvtt5XO5e+++44PP/yQTZs2+UJfTsIJsUH4eUj+24hUIDnzjwAaNWpE69atKS0t9SV9n3baaTRo0KDKbyUHSWDHjh2+IQUCCSDbHQ+JeWOJBKcYzM7OJisri6ysLIqLiysJwV9//dX3OR5vD8E8SLY7ONEFkmmavnPIFkiB3gyXL1/OY489BlheS/8HNCS+BymU9wjczUGyHybff/990GWChdegskCy84+ASp0ZEqWLvO3p2H///YP2lvInkT1IoRK0obJnMlbHwDRNn2AZPHgwRxxxBHv37mXatGm++bfccotv+UDPg3BCbBC+B8k+p+0CupEmajtrINnY99VXX30VqNp7zUY8SEKliqKBBJJTOOzatcunyJOR7du3U1RURFZWFk2aNAECvx3PmDHD9zkeF0eye5CKioowTZOMjAxfuGPVqlWVbuSmaXL11VdTWlrK+PHjfbWP/El2geRmiM1+8G/YsCGgNxgC92CzcfbKs/OPwHq4tGrVitLS0oR5SYo0vAbJ4UEKJpBSUlLIyMio1Csr2mzatIk9e/bQsmVLmjdvzsiRI4F9vdm+/PJL39AuEHg/hhtii9SDNHToUKD2ITbYJ5CKi4tp1qwZxx13XMDfOnOQEuXFoKa4LpCUUk2VUrOVUoVKqYO9085WSs1QSn2rlOroto2BiEQgQeAwXLLgzD+yCZRf4ZZA8vcgJUstJFsMZGZmkpWVRevWrSkuLvaJCbC8cl9++SVNmzblvvvuC7quRBJIJSUlVc5/u02BuvhDYggk2Jdo7U+wHmwQ3IMEiRdmq41ASkQPUnUhNoi9d9LplQMr9ATw+eefU1pa6vMeOYtu+hMrD5JtS009SIEEElhD6fgPOG2TlpZG48aNqaioiNuwKLHCdYEE7AGGAx8AKKVSgWuAY4BbgVuC/tJFIhVIyZyH5Mw/svG/8ZeUlFSq2Bppr4maEKybf3p6OtnZ2QlfC8nOUbDFgX3zc74d2r1FLrroopAjY8dSID3wwAMceuihYe/LCy+8kOzsbJYtW+abZj9cE9mDBDBv3ryAy4QbYrMTtG0SrSeb/8M8HJI5xAaxz2+zr1f7+u3Rowc9evRg+/btXH/99cydO5d27dpxzTXXAIGfBeHmIHXu3BmPx8O6deuCdoTZu3cvK1aswOPxcOqppwKWMI6kiKx/DpK9bZtAvdec1JU8JNcFkta6VGvtdK/sDyzVWpdoracDfYL81FUiFUiJ6J4OF2cXfxv/YpELFiyoVO7AzRAbJEeYzelBgqoCqaKigg8++ACAs88+O+S6YiWQysvLeeSRR9iyZQs//vhjtcsvXbqUd999l7179/rEHYQfYnMjSTscD1KoEFuXLl3o0aMHJ598cpXehYnWk60mHiRbmG/dupXy8vKY2FVTEtGDBPjCbE888QQAN910k++eVBsPUnp6Op07d8Y0TVavXh1wmT/++IPy8nK6detG+/btadWqFXv27GHjxo1htylUDlLz5s19obtg1JU8pFS3DQhAc8DZD7LKYEFKqQnABICJEycybNiwmBlTWloasEfW8uXLfZ83btxYZRlbOKSlpVFaWsry5curHTk7VgRrQ7jYYwtlZWX51mPfdJYvX05ubi7/+9//AKtWR0VFBevWrYtaT7ZA9peUlLB7925SUlICDghq39Tnz5/PgQceGBU7akOgNtg3uPT0dHJzc2ndujVgic3c3Fzmzp3Lhg0b6NChAx07dgy5P+1Y/6ZNm6Lag3DWrFm+8PDSpUurXfc999zj+/zRRx9x0UUXAftCAhkZGQHXYbvid+3aFbMekMGuA+fLzuzZswMuYwvt1NTUgPO//fbbSp0ZbOySAOHsu+oIdR0vXryYNWvWMGLEiKC/N03Td99q3LhxRPY0b96c7du3s2jRIt95WhNqey/yxz4u9jUUCDsUtHr16moFSHUEsn/RokWA5f2x5w0aNMg3v3379px88sk+7+T69eurrMN+yS4pKal2/3Ts2JHVq1cze/ZsX06oE3vsz5ycHHJzc+nSpQv5+fnMmDGDQYMGhXUMnPPtzz169GDAgAGMGDGi2oiIPZ7cH3/8UaUMhpNbbrmFvLw8nn/++bA7DUT7HHJGRvxJRIG0A8hyfK/yyqK1fgF4wfs1poNP5ebmBtyBTtfh9u3bqyxjuyj3339/fv/9d0pLS0MeiFgSrA3hYouPnj17+tZj1+3Jz88nOzub33//HYCBAwcyffp0SkpKotbeQPbbb2EtWrSo5NmyOeigg/jwww8pKChwbb87CdQG+2HVvHlzsrOzfeGZvLw8srOzfT3X7PopoejWrRtgudej2d5ffvnF93n37t0h171161Y+/PBDwHooLVy4kJSUFNq1a+crP+A8h5zYD66ioqKYHa9g14HT27tmzRoaN25cpdaR/Uadk5MTkX29e/cGrDfp2rYr1HV8yimnMH/+fGbPnk3//v0DLmMnE7do0YKDDz44om136NCB7du34/F4atWO2t6LnJSUlLBlyxY8Hg+HHnooqamBH2d2jmLDhg1jcgxsgX344Yf75rVr1442bdqwZcsWbr/9dnJycnwhsUDPC/v6OOiggwKWwXDSq1cvfv7554DrgX3e2sMOO4zs7Gx69eqF1tq3fDjHwPboNmvWrNKyzp7KobA9xaHOl507dzJ58mRM0yQ1NTVofqI/0TyHqsP1EFsAVgA9lVLpSqlBwG9uGxQIp4LNz8+v4nq2cxZ69uwJJHeIzdnF38Y/xGYnaNtvsLF2rQZL0LZJxhCb3UNl1apVmKYZdngNYhNiM02Tjz/+2Pe9Ohf9c889R3FxMSNGjPD1cLHHJ6suBykjIwPDMNi7d29cwzilpaXk5+fj8Xg45JBDAFi4cGGV5UKF2EIRrxDbn3/+CViJwcGoSXjNJhETtXNzczFNkw4dOgQVRxDbEJuzi78dIger99wrr7zCLbfcwt///ndgXy6X/7OgqKiIoqIi0tPTffeCUDjvE4GwE7R79eoF7DvekfRkC5SDFAnh5CBprX15UfHIWa0JCSGQlFJTgeOBF4HzgMeBH4C7vX+ucNNNNzF48OCA9VHst4a0tDQqKiqqCAL7gB900EFA3UjSdnox7M+5ubmsWbOGDRs20KxZM59rOdYCKViCtk0yCKRQSdpz5sxh7dq1dOjQgSOOOKLadcVCIC1cuLDS/gslkIqLi3n66acBuOaaa3xC2R6frLocJMMwXEnUtq/LNm3a+LroB0rUDtWLLRTx6MW2Z88enwfCDnUHojYCKdjD3U3CSdCG2A434t/F38nw4cO58847fbWImjdvTmpqKgUFBZUSrJ33snDCTIE6czgJJpAi6ckWKAcpEsLJQZo9e7bvc6IKpIQIsWmtTw4w+d24G+LHX3/9xdq1a1m4cCF/+9vffNN37dpFQUEBGRkZdOnShWXLlrFly5ZKsXn7gNsepGQWSIE8SA0aNKBt27bk5eX5wioDBw70vWHHy4OUzALJ34PUsmVLmjZtSkFBAc899xwAZ555ZsDCkP7YIaFoCiTbe/S3v/2N77//vlL5AX/efvtttmzZQr9+/TjmmGN8b7lfffUVO3fuZPv27aSmpoYUGJmZmRQWFlJYWEhWVlbQ5aKJ7RFp166dL8QZSiBF6kFq164dqamp5OXlUVxcXOM38lA4j8ucOXPYsmVLwB6PkY7B5iQRPUjV1UCyiWUHgEh6BXo8Htq0acPGjRvZsmWLz7sYbg0km1AepOLiYlauXInH46FHjx6VbIvEgxSom38khDMe26xZs3yfE1UgJYQHKVGx8wd++61ylM9ZF8i+ETkFkD1uk8fj8d2MEunNKxL27NnD9u3bSU9Pr/JwsC/wd9+1tOygQYPi1nshWBVtm2SohWQLJPsN1zAM383vjTfeAMILr0FsPEi2QLriiisAy4MUqKuwaZo8+uijgOU9MgyD/fbbjz59+lBYWOg7P9q2bRtS7LlRTdt+4Ldt29YXYgvUk62mIbaUlBTfi4UzGTya+AtXu4KzP/YDMpIu/jaJ2NU/nB5sENvzyvbihLtPA9VCCrcHm42db7h69eoq4ejly5dTUVFB9+7dfeLGWTsp3PB1bQVSXfEgiUAKgT38g91LwcYZcgokkJwnvP3mlaweJKf3yN/9a7+52fWPBg4cWOnNIZK6G5FS3U0lGWoh+XuQYN/NrLS0lHbt2lXqDROKjIwM0tPT2bt3b6VyCzVl1apVLFq0iKysLE455RQaN25McXFxQAH29ddfs3jxYtq3b19p+AE7zPbSSy8BwcNrNm6E2JwepD59+pCSksLvv//uGwIGrGNRUFCAx+OpkrwdDrEOs9kCyb4+7bwvf2oTYrNFSCJ5ZBMhxGZ7kJz5R6EIFKoMtwaSjT2OXmlpaZVzyj+8Zi/fsWNHSktLWbt2bVjbiJZACpaDlJubWylkLwIpCbE9SIsXL66kvJ2FEwMJJPtgt2zZ0hd227p1a1KWXQ+Uf2TjfHPzeDwcfvjhNGjQgMzMTMrKymJa06Y6DxLsC7OFe1MA64F54403RvSbmuKfgwT73OdghddSUqpUuQiIYRhR9SLZ3qMRI0aQnp7uu7EHykOya71ceeWVlarrDh8+HNj3phgvgVRWVsbzzz8flrfDKZAaNmxIz549qaioqPRS5Lyewwl3+hPrRG37mNjlTqZNm1bFU1BeXu4LyYT7MHdih2uc5U3cJlwPUrTOqzVr1jBt2rRKL36RepBCCaRIShAEy0MKJJCc9oUbZrOTtGOVg+QMr4EIpKSkefPmtG/fnuLi4krx3kAeJOdQIs4banp6Os2bN6e8vDwpi2YFGmbExnlj6tOnj68mRzzCbOHcVGqSh3TJJZfw4IMP+hKOY0koDxLAWWedFdH6YiGQTj/9dICQAsnu+nvhhRdWmj5gwIBKb8XxEkgff/wxl156KWeeeWa1Xkz7QWV7egOF2WwvQaThNZtYV9O2PUhHHXUU3bt3Z/v27VUeQOvWraOkpIQOHTpU2408ELbXaeXKlQlTLDLcHKRoeZAuvfRSLrroIp566inftJp6kAK9UEcikILlIQUTSJEmasc6B8l+abKvuzolkJRSzZRSgQdiqWPYSdbOPKTqcpCcAgkIuEyyEGiYERvnjckZCoqnQArHgxSuQPr+++/59NNPgfiMneWfgwT73vTatGnDkCFDIlpftATS5s2bmTlzJg0aNODEE08EggukwsJCtm3bRoMGDejQoUOleSkpKZx88r7+F9XVOYlWMq1dgHPGjBl8/fXXIZd1epCAKonapmly2223Afs8YpESrxCbXZAQqvZmq02CNljnaHZ2Nnv37o2LdzUcwg2xReu8sgvm3nDDDSxZsqRSF/9oeJAi6SEZqQcp0q7+sc5BsgW8fX+pEwJJKdVVKaWBfOAopdSPSqk7Y2NaYmBXYXYKpOpykPy7BNcFgVSdBymQQIrlSV9dN3+ITCCVl5f7xkqC6mv+RINAHqTBgwdz/fXX8+KLL4YdXrOJlkCaMmUKpmly/PHH+8RbMIHkfIsP1EXZWdm5Og9StJJpnaG1W265JaQXqTqBNHXqVL7//nuaN2/OTTfdVCN7Yh1icwqkk046Caiah1Sb/CObRAqzFRQUUFBQQMOGDav1vETjvCovL/e9GO/du5dzzz2XNWvWsGfPHlq1alVliJlgRCNJGwJ7kIqKili1ahUpKSlVjnOkIbZY5iCVl5ejtQbwna91QiABzwIdAAOoAH4CRkfbqETC9iA5cxKqE0j+HqRErCESLoG6+NskggcpWgLp9ddfZ8GCBT6xEqpLe7QIJJA8Hg8PPvigb5DJSIiWQPrkk0+AfeE12CcgQgmkQBx//PG+In7xCrE5r7PZs2cHTVqGqgLJHg5o0aJFFBUVcf311wNw6623hvRWhiLWITb7mHTo0IGjjz6ahg0bMm/evEpCMRoCyf5tIggk24sVTJg7CRZimz59Oj/88ENY28vLy6O8vJymTZuSk5PDb7/95gspR5LTFcscpGXLlmGaJvvvvz8NGjSotHykIbba5iBlZmaSlpbGnj17qnQaWbZsGYWFhXTu3Nn3fK0rAmkQ4EzOWAWEHgMhyQkUYnMKJDsJO5BAsnMW3PQgTZs2jalTp/pu+JESyoPUrl07Dj/8cIYMGeITIxAfgRRJknZ1Amn37t3cfPPNADz44INA8C7t0SRQknZtsAWSXTSwJpimycyZMwE44YQTfNPtG7u/cKxOIDVr1oxTTjmFtLQ0X6/QYERbINn233rrrUGPpb9AysrKonv37pSUlHDttdeydOlScnJyuPzyy2tsjzPEFotzyulBatiwoa9m25dffglYOWLvv/8+UHc8SHa4K5xxFgOF2MrLyzn55JM58cQTq4zjGAhnOO+NN94gJSXFNwxPJGUTYpGDZJomFRUVTJ48GagaXgPo2rUrKSkprFmzplKRymDU1oNkGEZQL5IdXnPmKCZqfm6kAikfsAfxaYPlPYp9LMJFcnJySEtL488//6SwsJDi4mLy8/NJTU2lTZs2CZ2DNGXKFE488UQmTJhAnz59aNSoEZ07d/blVIRDKA+Sx+Nh1qxZ/PTTT5Xe4uJx0ofz1hVuLaSHH36YjRs3opTi0ksvpVGjRuzZsyesG2dtCORBqg3R8CD9+eefFBQU0K5du0o5RfY5HKkHCSzv3MqVK+natWvIbUdbIN122220a9eOefPmMWXKlCrL7d69m127dpGenl6p+76dqP3ss88CcP/991fqnRcpTZs2pXHjxhQWFka1ThVY4Z5t27aRmprqeyGz85CmTp3KM888w1FHHcXmzZs58sgjK4neSEkkgWSP/WiPVBCKQCG29evXs3PnTvbu3cuCBQuqXYctkOzK9rfccotvXrQ8SJHkIDVv3pwWLVqwZ88eVq9ezZgxY3jyySfxeDyMHTu2yvLp6el06dIF0zTDyiGrrUCybYSqzwE7Qfvwww/3tfmvv/6K+QtpTYhUIL2IJYoM4E1gGPB8tI1KJNLS0nxepMWLF1cSDB6Ph2bNmlUpH58IIbbCwkKuvPJKwLrhH3DAAaSmprJu3TruueeesB7+paWlbN68GY/HE/ZAghB7D5JpmmF5kOxaSBUVFUGL9G3cuNHnNXr00UfxeDw+YRDrMFugJO3aEA2BZOfe2Lk4NuHkIAUjMzOz2kRaezmofTKtfZ116dLFlzd02223VSmz4ezB5hT4zrYPHDiQM888s1b2GIbhE4fBhoeoKfY56izCaed1fPDBB0ycOJHS0lL++c9/8t1339VK6CW7QHKeV87jEKhyuj+2QLLDxDfffLNvCCBbUIdDq1atMAyD/Px8ysrKgJqF2GCfF+moo47ivffeIysriy+++IJTTjkl4PK299DuxBCKaAik6jxI/mVhdu3aVeNtxYqIBJLW+j7g78AHwIfA37XWD8XCsETCDg389ttvVUJOHo/HF2azk7MTwYN0++23s379eg477DA++eQTli9fzp49e+jfvz/l5eVMnz692nXYIYG2bdv6xhMKh1gLpMLCQsrKysjMzKz2hm+H2YLdFB577DH27NnDGWec4es1Zt8EY52onYgeJPthcdhhh1Wa7hRIzje9cLtah0M0kmnLysrIz8/HMAxat27NP/7xD7Kzs/ntt9+q5CL5d/G3cQqkRx55JKzxsarDfpDbvYyihS2QnN6+bt26ccABB2CaJo0aNeKtt97i8ccfj+gaDkTnzp1p0KABGzdujGmNs3CwBVKgcJI/gTyTzlycSASSvZ9TU1P56quvmDp1aqWOCNVhD7djmmaV50WkAsn2XOXm5tKtWzdmzpzp6xUWCFsg2QMbh6K2OUgQ+DmwZ88eFi1aREpKiu86c3qREo2Iu/lrrf9Paz3K+/daLIxKNOyCkYsWLQpYF8hfAIUrkDZu3Mjjjz8ecPTw2rBw4UIef/xxPB4Pzz//vK83VFpaGsceeyxAWMmJdn0bf29CdcRaIEXyxmV7//zrwtjY3aGvuuoq37R4e5ASUSD5H/OGDRvSrFkzSktLK93IbIHUuXPnGm/TJhohtq1bt2KaJq1atSI1NZWMjAxffpmdo2Hjn39kc+SRRzJ48GCuvfZaBg4cWGNbnBx8sJWZECuB5J8Af/fddzN8+HBmzZrFmDFjorKtlJQU30M5knG9ok1JSQl//PEHhmH4vFqhiKYHySlEmzRpwkknnRSxgHZGFIqKiigqKiItLS3i+4B9jQ4ZMoRZs2ZV602zc6XCEUjR9CA5nwPz58+nvLycgw8+uNIYlFAHBJJSqjzAX1ksDFNKPaCU+lkp9bpSqnavPrUkkAfJmZPjL4D8u/kHC7HddNNN/Otf/6Jfv34ceuihPPXUU77f1pSKigouueQSysvLueKKK6p4Ao455hgAfvzxx2rXZSchHnnkkRHZEGuBFE54zcbOubATVp1s2rSJJUuW0KhRo0oPwnh5kGKVpF1TgWSaZlCBBPseDvZ+KS8vD5nEHynREEj2NWZfc7BvPLuFCxf6whoQXCA1atSIX375hYcffrjGdvhjezoWL14ctXVCcIF09tln8/nnn/uEWbRIhJ5sK1eupKysjK5du9KoUaNql3eeV7b30ymQli5dWu05F0gg1RRnorZ9L2vZsmXEQuuqq67ip59+4ptvvgmriKl9DvqPLRqIWOUgOcNrNnVGIAFLgd+9fxuxcpGqD2hGiFKqL5CttR4CLAMiKykcZZxjstkXSjAPkmmaVZLugnmQfvrpJ8C6gOfPn89VV11Fdna2r5t1TXjhhReYNWsWHTp04O67764yf/DgwaSkpKC1rjbm+/PPPwPxFUh21fJQCXuReJCGDh1KamoqM2bMqCIcvv32WwCOPvroSqG6eHiQKioqfG7scG7y4VBbgbRhwwby8/Np2bJlwOEb/AVSXl4epaWltGnTplaueJtYCaRWrVrRrVs3ioqKfKEZCC6QYoH9cIq2B8k+FtWVUIgWiZCHFEn+EVier4yMDEzT9F1ztkBq2LAhFRUV1YqGaAokZy2kmuYfgZVjOWTIkLDzygYMGECDBg34/fffqxUjscpBshO0BwwY4JtWZwSS1vpgrXVv718nYBIQOHZROwYBX3k/fwkMjsE2wqZ9+/a0aNGiUgl/p0BydvUvKCigvLycJk2a+E7crKws0tPTKSws9A2CuXnzZlavXk1mZiZ5eXm89957DBs2jJKSEi655JKgg/w5Wb9+PWeccQaHH344AwYMYODAgVx33XWANT5WVlZWld80adIEpVS1eUjbt29n8eLFNGjQgP79+4e5pyxqI5DOP/98unfvzqBBg/j4448DDmsQiQepadOmDBo0iPLycr755ptK8+zv9hhWNtH2IP34449VcqDs86BRo0Y1Gt8rELUVSE7vUaC3WX+BFM38I4hOknYggQT73lidI4jHUyDl5OTQoEED1q9fX6syDP4EykGKJckokKBymK2iosJXYNGujh4qzFZaWsqmTZswDKPKeVUTnBGFmuYf1YSGDRsycOBATNOsNoIQixyk7du3+1I7ksWDlBrJwkqpM/x+q7B6skWb5oD9+l4AVDp7lFITgAkAEydOrPKAiyalpaVs3LiRHj16MHPmTF8F0AYNGvjykWyVvWrVKt8bYrNmzXzzwXqL3bhxI7/99hudOnXy5b7069ePHTt2MGjQII444gjOOussZs+ezdVXX829994b1K7t27dzxhlnBCz8ddxxx3HEEUf4tl9aWlrJlkMPPZRZs2bx2Wef+fKr/LG9K3379o047GdfXNu2bWPDhg1hu45/++03PvzwQ8DKfzrjjDPo1q0bl1xyCeeee65vPfbNzXkMQjFo0CB++uknPvzwQ18ozTRNpk2bBlg5Zs712MJ2zZo1Ya0/FL/88gujR4+md+/elYZ/sMfua9iwYa23YePc7zVZp33z2n///av8vrS01DfW3rJly8jNzfWNV9a6deuotMEWjTt27Kjx+uwHd+PGjSutw36wf/fdd75eXnZ9rNTU1Kgdg1B0796dJUuW8OOPP1YJfYeD/3UM+zofpKenx6UN9kPP2aM3EgK1IVLmzp0LWC8y4a7LvkevXLnSVwuoTZs29O3blw8++ICff/6Z0047LeBvN2zY4OusAtTaflt0rFy50ldeolGjRnE5focddhg//PADn332WSUvjj/Oe0moEinhkJuby/r16xk3bhx5eXn06dOn0vPR7jywevXqsPZBNM4hJ4FK2NhEJJCweq85Yx8G+zw90WQHYLs/mgKVXBFa6xeAF7xfY1o8ITc3l+zsbJRSzJw50xf6OeSQQ3w71k5ctJPtwHKjOnd8+/bt2bhxIx6Ph+zsbF+S4zHHHFNpuZdffplDDjmE119/nYkTJwb03hQWFnLmmWeyYsUKDj74YJ5//nk8Hg8VFRUYhsEhhxxSyTVqt8HmlFNO4dlnn2XevHlBTw77Le3YY48NeQIFIyMjg+LiYlq0aBF2CGnixIkAXH755fTs2ZNHHnmEP//8kxtvvJGOHTty7rnnVlq+Y8eOYdl2zjnncP/99/Pzzz/ToUMHDMNg6dKlbN68mTZt2jB06NBKIs4WjX/99VeN2m5TXl7uE7lr1qyptC7bhd2kSZNabcOJ/fDauXNnjdZpi+2jjz66yu9zc3N9+Se7d+8mOzvb5+np0aNHVNpgl54oKSmp8frs/ZqTk1NpHcOGDeOOO+5gyZIlvum2p61Xr15ROwahOOSQQ1iyZAlbtmyp0fb8r2PY5009+OCD49IG++G+evVq37UUCYHaECl2kvGRRx4Z9rqaNm3Khg0baNKkie/l5IADDuDYY4/llltuYfny5UHXZYvQLl26kJaWVmv77WTpoqIi3/7Lzs6Oy/EbOXIkjzzyCHPmzAm5PbtkTbdu3Wrc+9EuQ1BUVMSLL77Id999R8uWLZkyZUqlEL5dAqO0tDSsfRCNcyhcIvXt3+n4uw24EDg95C9qxgzgOO/nE4Dq+6THGGcVYP+6QM4cI/8ebIGWAXzViv17yRx88MH861//wjRNLrvssiohppKSEs466yxmzZpF586dmTZtms/7NGjQIAYOHFht3NjOQ5ozZ07QcIadoB3pgKk2kY7H9ttvv/HJJ5+QkZHBLbfcwsSJE1mxYgX//ve/Afjoo498y0Yat+/bty/t2rUjNzfXlyRrh9eOO+64Kjd5Zw5SbYqXvfrqq77chl27dlWqZB7tHmxgidL09HSKi4urlPcPh1AJ2hC/EFu0c5DAEicpKSksXrzYt/5g3fxjRSwSteOdg9SiRQtatWrF7t274+Lx8KesrMznJQyniraNM8Rm5x91796dvn37YhgGixcvDlphOlDeaW2IVg5STTj88MPJyMjwCfVAlJWVUV5ejsfj8Q0TVBPsNv3666/cddddeDwe3nnnnUqjLkBih9gizUG6XWt9h/fvbq31G1rryMevqH47C4A8pdTPQC+smkuu4hRI7dq1q6Sq7RN+69atQQWSM+5cUlLiC9XZxcac3HrrrXTq1Im5c+f6qvmC9cY7btw4pk2bRuvWrfn6669rlHuQlZXFoYceGjQPqbi4mDlz5mAYRo27OUeah2QnlE+YMMH3wEpNTWXChAmAJWhqWljNMAxffRA7zOUUSP5kZWXRsGFDX6XlmrBr1y7+85//+LYPlXsxRrtIpL0d22UfaZ7Lpk2b2LRpE1lZWXTr1i3gMskskBo1akTPnj2pqKhg3rx5mKbpy0GKRl5JOEQ7Ubu0tJStW7fi8Xh896B44GYe0qpVqygtLaVz584RXTvO/DbbU9q9e3cyMzM58MADKSsrqzTephO7p2agjgs1wa0cJKBSTmmwUi/O/KPa1ABzerQB7rvvvoD326QXSEqpnSH+opdx6EBrfb3WeojW+jytde2CoFGgV69evpPF/00iUg/SggULKC4upkePHgHLyzdu3Jgnn3wSsCq23njjjb6y7G+//TZNmjThyy+/jGgMIH9CdfefM2cOJSUlHHzwwTUeoDMSgbR06VI++OAD0tPTueGGGyrN69q1K127dqWgoMCXYBtJkraNLZC+/PJLysrK+P7774HAAskwjFr3ZLv//vvJy8vjiCOO8OWbOAcPjYUHCWqeqG3nEx1yyCFBk8aTKUk7kFfIHoh29uzZvsr3TZo0ifoxCEa0ayHZbW3Tpk2t3vQjxQ61ulELqSYJ2lC5CKntQbLvn7bHNFiitu1BioVAqskwI7XFHljcvgf6E40ebFBZ9J199tm+gZ/9SXqBhJUD9FeQv8QcZS7KZGZm+mKq/gLJ2YvNvwaSjVMgBQuvORk5ciQjRoxg586dPPjgg8yZM4eUlBSGDBnCl19+GXHxRn9sgRToLaK24TUILpDmz5/PE088USm5/J577sE0TS666KKAsWXbVruWUU3c0sOGDcPj8fDLL7/w3XffsWvXLnr06BH0pucvBiJh7dq1PPLII4BVqdsOfySyQKouvAb7wjibNm2ioqIi6gKpQYMGpKSkUFpaSmlpaY3WEcor5BRI8ezBZtO5c2caNWrEpk2bolIjLFgNpFjjpgeptgLJP8QG8RdIzmeB/byIlwcJ4ieQmjdvzsCBAxk0aBCvvPJKUG9UIguksF47tNZdYmxHUtCnTx9WrlxZRSBlZmbSsGFDioqKfAMB+hfucr412De2UALJMAyee+45rrvuOjp16sTQoUM58sgjo/ZAPfLII/F4PL48JKe7uqYFIp0EE0jjx49nwYIFXH311SilGDFiBG+//TapqanceOONAdd1zDHH8OqrrzJt2jTuvPPOGnmQWrRowYABA5g5c6ZvfK5A3iMbpxiIlEmTJrF3717GjBnDEUcc4Tv2ToEU7SKRNrEUSA0aNKBly5b89ddfrF69mm3bttGgQQPfC0JtMQyDzMxMdu7cye7du31tCRf/YUb8sQXSrFmzXBFIHo+HXr16MWfOHJYsWVKrFxCIf/6RTTIKJPs627Vrl68XrP3CG2+BlJGRQdOmTSkoKPAlnMdTIPXp04fMzEyWL1/Oxo0bq6RpREsgeTweXwpHqFBdIgukiAuwKKUGKqUmKKWu8v5dGQvDEhH7hubfNd4wDN9bwdKlS4HQHqQZM2YA+5R8MLKzs3n77bd58MEHOeGEE6L6MLXzkMrKynz2gFXA0D6poy2QysrKfOGFJk2aoLXm9ttvp6KigrFjxwYdrmLgwIGkp6czZ84c8vPza5zYaHfvtrsJhxJINfUg/f7777z99ttkZGRw3333AfsewrHOQYLYCiTYt1/semD77bdf1Oo4Qe3ykPLz8zFNk5YtWwYMOe2///5kZmaydu1a3/A+8RRIUPNE7ZKSkiqjsMe7BpKNmwLJvn+EMwabE/s6W7FiBUVFRbRu3dqXr2cPNvvbb78F9FxGWyDBvueBvQ/jKZDS0tI46qijgMBepGjUQLIxDKPaPKasrCw8Hg+7du2qdUmBaBPpUCO3Ar8AzwKPef8ej75ZicnEiROZP38+48ePrzLPPuGXLVsGBBdICxcuZP369WRlZUX8FhRtAuUhLV68mIKCAjp37lyrG4LdfqdAWr16NaWlpXTq1Im8vDzef/99zjjjDAYMGMBtt90WdF2NGjXiyCOPxDRNvvnmmxoLJOdAjh6Px9f+QNTUg2Qn359yyik+wWc/hBM1xPbXX3+xdu1aGjZsWO3YVvbD2B6nL1rhNZvaCKRgCdo2KSkpKKUA+Oyzz0IuGytqmqj9z3/+k8GDB/PVV/uqqrgVYsvJySElJYU1a9bUqLdkTSkvL/fdX+0xFsPFFkgLFiwAqJS/2bRpU7p3787evXsrVVoHq7v7li1bSE1NjaqYts87e//FMwcJ4G9/+xsQWCBFy4MULh6PJ+bDU9WUSF/9/oFV2RrgPmAREL0BixKc1NRU+vXrF/CN2RZAdugkWC82O+Y8YMCAqL551wRbIDz99NO8+eabQHTCaxDYg+TsntuwYUPOOussPvzwQ3799ddqxZgtbj7//HN27dqFx+PxFS4Ml8MOO8wX+uzfv3/IEE5NPUh2foNTaCS6QLK9R/369fMNbByMQB6kaFKbRO1wuu3bFXztl4J4e5BqkqhdUFDA//3f/wH4Om+AewIpPT2drl27YppmpTHNYs3q1avZu3cvHTt2DDhKQCjs88oWSHb+kU2wMJvdg61Dhw7VXhuR4C/M4+lBgsQSSJC4YbZIn9BtgM+9n2cB/8XlcdISBf9utv4CyT8nKVqjhNeGE088kTPPPJOdO3dy/vnnM2bMGKZOnQrERiDZb3/hjMDtjz3o7JQpUwAr/yhSgenxeHxCq7rq6zX1IPnnNwCu5CBF0s3ffiiEU93Z3i92r7doCyRnb6NICafbvl092C4ZkQwhtvfff98X9vjf//7nS453KwcJ3Bm0tqb5R7DvvLLzF8MVSLEIr0HlczQtLS1uPSltDjnkEJo2bcqff/7pO59sRCDtI1KBlA9UYFW6fgq4A2tYkHpPdQIpLS2t0ltCdflH8SAlJYX333+fl156iczMTN555x2++OILoHY92CC0QIqkwJtN7969ad++vU9Y1LT8wD333MP111/PtddeG3K52nqQnDfgUB6kRMhBCjf/CPbtFztXI5lCbFB5DCiIv0CyvR/5+flBC/X58+qrrwLWNVVRUcErr7wCuJeDBO7kIdVGIPkLELcFkvN50aJFi1rVG6oJKSkpQfOQbIEUjRykcKkrAulxLHF0B9AJaAXcFV2TkhNnr5m0tLSADz7njTvUODjxxDAMLrroIhYsWOCzqWXLlhHH+P2pLsRWEzuPP/74KuuPlP32248HH3yw2h5SNfUghRJIeXl5vsrciRJiKykp4bvvvgMIa1Bi/4dxsgmkjh07VhJF8RZIhmFElIe0fPlyZsyYQePGjX2lI15++WXKy8tdC7HBvlCh3eEhHkTDg2QTTCAtWLCg0ugF0a6ibeM8R+Odf2QTLMxmeyvFgxS5QHoAuAVoBHQBmmmtH4u2UcmI842gVatWAd8I7GUOOuigiLswx5ru3bvzyy+/8Oyzz/Luu+/WOj8q2iE2qJxkXVMPUrg0bdqUhg0bUlhYGHY17W3btrFt2zYyMzMr3QAbN25Mo0aNKCoq8q0rUQTS1KlTyc/P5+CDDw6rZ1CyCyTDMCp5keItkCBwmK2iosJXcdiJnXs0atQohg4dSk5ODhs2bOCLL74Iq72xwvY+/PTTT1RUVMRlmzXtwQbVC6SWLVvSpUsX9uzZU0n0xSPEFu/8I5vBgwcD+8LlNhJi20ekT8GHgYbAvcBq4HOl1EVRtyoJcQqkYG8E9jKJkH8UiNTUVC699FKGDh1a63X5j8X2119/kZ+fT2ZmZo0HGhw2bJhPeMb6pmIYRsReJDv/qHv37lUEsu1htMNsiSKQJk+eDMC4cePCcvP7C6RoPzhqk6Qdbm0jp0CK5xAdNv6J2vPnz6dPnz60bduWr7/+2rdceXk5r732GmAdH4/Hwz/+8Q/AChVXVFTQqlUr0tPT49yCfYMB5+fnR60yeCgqKip8JVRq4t12XmctW7YM+IJ16qmnAvDuu+/6pkV7mBGbRBBI9rnvf68QgbSPSMdiu0FrnQMo4CXgKOD5WBiWbIQjkGw37imnnBIXm9wkMzOTtLQ0ioqKKCoq8oXXevToUeN4e8uWLX3dtONxU4k0DylQeM3GXyAlQqHIrVu38sUXX5CSksJ5550X1vqd4qN169ZRz1OoTZJ2uB4VWyC1atWqxiOV1wbbA7Jw4ULuv/9+BgwYwJIlSyguLmbUqFG+ITy+/vprcnNz6d69u6/TxLhx40hNTfUNu+NG/hFYLxChqvFHm7Vr11JUVET79u1r5D12epACXZ8AY8aMASyBZHvF4pWD5AbB7hUikPYRaR2klkqpi7E8SH8HDGB9LAxLNsIRSNdccw3Lly9n5MiR8TLLNQzD8F3427dvr1WCthP7La9r1661MzAMIvUghSOQ7Id4IiRpv/XWW5SVlXHiiSeGHWpKS0vznevRDq9B7ENsYHWQ6NevH2ed5U4HXNuD9OuvvzJp0iRKS0u54oorOPXUU9mxY4fvfyDvXtu2bSvdP9zIP7IJ1VU82tS0/pFNOAJpwIABdOnShdzcXF+5k3iE2NzKQcrKysIwDHbu3Fkp7yqahSLDJVEFUqQjHG7GElXbgVeAN7XWv9R040qppsDXwEHAEVrrxd7pZwP/AoqAsVrrDTXdRrxwJmkHO+HT09N93WPrAy1atPANyFibBG0nN954Iz179vRVxY4lkXqQAnXxt7FFRbxCbNu3b6eioiJkLpmd3zJ27NiIttGhQwe2bNkStPJ5baipQCovL/fVGKtu6JPMzMwqeRfxpG3btr4hW9q3b8+rr77KCSecwK5duxg8eDCLFi3ijDPOYMaMGRiGwYUXXljp9//4xz/48MMPAXcFkrPQbHXnWm1ZvXo1AN26davR753XWbBBvg3DYPTo0dx///288847KKXYtm0b6enpURtOx6Zx48a+4anc8iB5PB6ysrIoKChg586dPs+ceJD2EekZ/RFwOtBea31ZbcSRlz3AcOADe4JSKhW4BjgGuBUrKTzhSU9P95Wud+uNINFwJmrXNkHbJi0tjTPPPJNGjRrV2r7qsB8+0Qix2XWwYi2QGjZsSOfOnSkuLuann34Kutxvv/3G/Pnzad68ecQhX3u/JJIHKT8/35eT40bYLBIMw+Cxxx7jn//8J4sWLfLV+GrSpAmffvoprVq14vvvv2fv3r0cd9xxVbwXw4YN84lTNwVSt27d6NSpE9u2bWPRokUx3ZY9zEqXLl1q9PtwPEgAo0ePBqzaU7Yo69ixY9TFn2EYPi+SWwIJAnucRSDtI9IcpHO01p9qrWs21HbV9ZVqrbf6Td4fWKq1LtFaTwf6RGNb8cD2EohAsggkkGrrQYontgcpmiG2WAskwzA4//zzgX0eokDY80aPHh3xjdB+YCeSBymcIpGJxAUXXMDjjz9e5V7RpUsXPv74Y5/I+/vf/17ltx6Ph0mTJgFw9NFHx97YIMQzD8kWSDU955zXWSiB1KdPH3r27El+fr4vxBnt8JqNfa66+bwQgRSaSENs8aA54OzvWqW+u1JqAjABrPHRqquKXBtKS0vJzc0Na1n7ZEtJSQn7N/EgkjZEE/sCW758OX/++SeGYdCoUaOIbXHLfvshtXr16mq3X1hYSF5eHg0aNACosrwtFteuXUtubq4vSbugoCDqAzSecMIJ3HPPPbz33ntMmjSpiggrLS319Y46+eSTw9q3zmMwevRoSkpKOPbYY6N+XPbu3QtYCeSRrNvuSdWsWbOgv3PrPIqUrl278tJLLzFnzhwGDhzos9lp/4gRI1ixYgUNGzZ0tU39+vXj9ddf53//+19YOV01PQZ24npN7h82jRs3prCwkMzMzJDrOPnkk1m6dCnPPfccYD28Ax2D2nLggQcyZ84cWrduHddj6GyDnWe0YsUK3wu+Hareu3dv3Oyyr/tt27axYcMGX87dzJkzycrKomfPnj4vXrSv41C9quMikJRS7YB3AswarbXe7DdtB+AcaKfcbz5a6xeAF7xfzWjYGIzc3Nywu6X37t2bOXPm0L9//xp3ZY8FkbQhmtjF1VasWEFZWRldunQJ+fYWDLfs79PHcl5u27at2u3bYzzl5OQEfOO0k6B37NhBu3bt2Lt3L4ZhkJOTE/UqutnZ2QwaNIgZM2bw66+/Vslh+fzzz8nPz6dnz54MHz48rO07j0F2drYvQTfa2GG7ioqKiI65nWS63377Bf2dW+dRTbjwwgurHLdEtP/000/n2muvZfbs2bRv377aUFRN22B7cWtzb33uuefYtWuXL0k+GBMmTOCRRx7xvcT06NHDt81oHoNXXnmFhx56KOr5TdXhbIPtxUpLS/NNs8eca9euXVzPt8zMTHbv3k2TJk186Sr33HMPCxYs4Ntvv+XYY4+tYn+siYtA8oqgY8JcfAXQUymVjlVO4LdY2RVtHn30UcaPH58Qw4gkArbXZObMmUByhdcgshykUOE1qJyk7QyvxWqIgXHjxjFjxgwmT55c5UFrD10xduzYuA9xUB01DbG5WTSxPtO1a1c6d+7M2rVr+e233+jXr1/Ut1FcXMzmzZtJTU2tVVmDcEtZHHDAARx66KG+YUdiFWLzeDxxF0f+JEqIDSxP3e7du9m2bRtNmzZl69atLFiwgIyMDNeeqe4OJw8opaYCxwMvKqXGefObHgd+AO72/iUFTZs2ZfDgwQn30HELO65sDxGQbAKpWbNmZGRkVKqmvX79es4///wqCdCherDBviTtvLw837piOUDlqFGjyMjI4Pvvv/flbwB8++23fPTRR6SmpvpylRKJ2gokNypj13fsPKRYdfe3B1Pt1KmTz7sRa+yaSBD9YUYSiUQTSLAvD8keAmnIkCFxt8XGdYGktT5Za91Baz1Qaz3ZO+1drfUgrfWxWmups5Sk+PfOqG0PtnjjX027tLSUc845hzfffJMbbrih0rLVeZAyMjJo1qwZZWVlvtoqsRRITZs25fTTTwfw5Rtt377d16X/1ltvTbhwDdSfJO26hB1ujVWidm0TtGvCqFGjfJ9j5UFKBAIJJDfqIEFVgWRXlT/uuOPiaocT1wWSUHfxF0jJ5kGCyj3ZbrvtNl+4cNasWaxZs8a3XHUCCfY9vG1vU7SLRPpji6HXXnsN0zS5/PLLyc3N5YgjjvD1gko07H3iHGrENE3WrFnjG+g3EBJicw9nPSRnwcFoYV9n8RRI++23H//4xz844ogjaj1wdyKTqB4k0zRFIAl1m2T3IMG+PKTXXnuN+++/H4/HQ9++fQF47733fMuFI5Ds8I8tkGLpQQLrxtKhQwdWrlzJVVddxTvvvENmZiZvvPEGqamJ2IE1sAfprbfeomvXrpx22mns2bMn4O9EILlH586d6dq1KwUFBSxcuDDq669tDaSa8sILLzBz5kxfz9S6SKIKpJUrV7Ju3TpatmwZk7y2cBGBJMQMp0DKyspKyvwQ24P0yiuvYJomt99+O7fffjsA77xjdcwsKipiw4YNpKamhiyeaLf/zz//BGIvkFJSUrjgggsAePrppwF4/PHHg+ZJJQKBBNKzzz4LwKeffsqwYcPYtm1bld+JQHKXWNZDcsODVF9IVIH0zTffADB06NCYVmivDhFIQsxwCqQDDzwwKZPXnZWKjz32WG666SZOPPFEsrKymD9/PitWrPAJnq5du4b0zMTbgwSVhxE59dRTueiii2K+zdqQnp5OamoqZWVllJSUsGrVKqZPn06jRo3o1KkTM2bM4Mgjj/TlcYHVxX/rVqverHNMRCF+2HlIb775pm+g12jhlgepPpCoOUi2QHIzvAYikIQYkpWV5et1kozhNdiXoNm6dWtef/11UlJSyMjI4LTTTgOskb/DCa/BPu+GvXysc5DAGtzz7LPPpkePHrz44otJIVLt/bJ7927eeOMNAN/YZL169WLp0qUMHDiQr7/+GtM0fcOMtGzZMuGHGamrnH766WRnZzNv3jxfGYlo4UaSdn0hET1IW7Zs8fVgi2UR6HAQgSTEDMMwfAMgJmOCNlg3/muuuYapU6dWqsFyzjnnAFaYrbou/ja2B8nucRUPDxJYuVLLli1LGu+KvV927drF66+/DlhDc3Ts2JGff/6ZIUOGkJuby/HHH8/AgQN9IkrCa+7RuHFjHnroIQAmTZpU6YFbG+yqyYZh1Onu9m6RiALpu+++Y8eOHeTk5LjuNRSBJMQUO8yWrAKpUaNGPPLIIyilKk0/7rjjaNGiBUuWLGHKlClA9R4k/xyseAmkZMPeL99++y2rVq2iffv2DB06FIDmzZvz1Vdfce+999KqVStmzZrFddddB4hAcpvRo0czZMgQtm7dym233RaVdW7YsMFXVT09PT0q6xT2kYgCyR7qxO3wGohAEmLMoYceSkZGBgMGDHDblKiSnp7OGWecAeArGikCKTrY+8UeC+u8886rVCAwIyODSZMmsWbNGh5++GGfMErk5PP6gGEYPPXUU3g8Hp555hkWL15c63VKgnZsycrKwjAMdu7c6SvR4HYOko3b4TUQgSTEmDfeeIONGzcmZFHC2jJ69OhK38PNQbKJRw5SMmILpNmzZwP4euIFWu7aa69l9erVfPLJJzzwwANxs1EITN++fbn00kspLy/nqquuClm7KhwkQTu2eDwesrKsoU937txJWVkZ5eXleDyeuJcCcXbqMQwjZuM9RoIIJCGmpKSk+PKQ6hpHH320L6/H4/FUexNv3bp1pSRp8SAFxikc+/bt6xs0OBgNGzZk5MiRVepuCe5w11130aJFC77//nv+9re/MXDgQHr06EG7du147LHHIlqXJGjHHmeYzRlei3eHjqZNm/q69B922GEJcT2LQBKEGpKamsrZZ58NWJV3qysol5aW5huTDUQgBcO5X4J5j4TEpUWLFtx7772AVV37119/5Y8//iAvL4///ve/EQ0jY4fYxIMUO4IJpHjj8Xh8oigRwmsgAkkQasXYsWPxeDwcccQRYS3vzEMSgRQYe794PB7OPfdcl60RasKECRP43//+x6effsovv/zC0qVLOfzwwykqKuKLL74Iez3iQYo9ToHkVv6RjX1/TIQEbQBXxxtQSh0OPAGUArnAhVrrUqXU2cC/gCJgrNZ6g4tmCkJQ+vfvz5IlSyqVAAhF27ZtWbRoESACKRj2fhk2bFilQp1C8mAYBieeeGKlaeeeey6zZ8/mnXfeqTQYbCgkSTv2JIoHCeCRRx5h7ty5CZF/BO57kNYDx2qtjwLWACOVUqnANcAxwK3ALa5ZJwhhcOCBB/oSHavD6UGSJO3AHHPMMTRq1MjXfV+oG5x99tkYhsHUqVPZuXNntcuXl5f7KqaHGsJHqB2JJJCOP/54Jk2alDAFbV0VSFrrTVrrIu/XEqAC2B9YqrUu0VpPB0JnaApCEiEhtuoZNWoUhYWFCeNmF6JDhw4dGDBgAHv37vXVDgvFpk2bKCsro23btq6FfOoDiSSQEo2EGNJbKdUZOB64G+gPOF8vUgIsPwGYADBx4sSYJnTZlVyTmWRvQ7LbD/va4LzxFBYWJk276tIxSFaS3X6A4cOH8+uvvzJ58mSOPfbYkMvOmTMHsIRVorS7LhwD/zbYPcfWrVvn89ilpKQkbDujfQxClaCJi0BSSrUD3gkwazSwB3gdGOfNP9oBOOMV5f4/0lq/ALzg/Vq7QhvVkJubm/Q1fJK9DcluP+xrg3NMum7duiVNu+rSMUhWkt1+gFNOOYXbb7+dn376iYyMjCrFAZ3s2bMHgP333z9h2l0XjoF/G+zwZUVFhS/sn5WVlbDtjOcxiItA0lpvxsopqoQ33+hT4A6t9XLv5BVAT6VUOqCA3+JhoyDEA8lBEuozrVq1YujQoXz11Vd8/PHHXHzxxUGXlQTt+CAhtuC4naQ9BhgA3KKU+kEpdY7WuhR4HPgBK+R2t3vmCUJ0kRwkob7jHOg5FFJFOz6IQAqOqzlIWuvXscJr/tPfBd6Nv0WCEFvs4UZSUlJk8E2hXnL66adz6aWX8v3337N58+YqYxTaSA2k+BBIIElSvIXbHiRBqFe0atWKUaNGcdFFFyVMV1ZBiCfNmzfnxBNPpKKigg8++CDoclJFOz7YAmn79u2+QpHiQbIQgSQIccQwDN59912ef/55t00RBNeww2yvv/56wAFtTdNk3bp1gHiQYo2E2IIjAkkQBEGIK6eeeiotWrRg9uzZvPTSS1Xm5+XlUVxcTMuWLaUzQ4wRgRQcEUiCIAhCXGnSpAlPP/00ANdcc40v38jm+++/B8R7FA+ysrIwDINdu3ZRWFgISA6SjQgkQRAEIe6MHj2aM888k8LCQsaPH09FRQUAL7/8MhdccAEAI0aMcNPEeoHH4/ENlZSXlweIB8kmISppC4IgCPULwzD473//y48//sh3333Hs88+S15eHnfddRcAkyZN4rbbbnPZyvpBs2bNKCgoYPPmzYAIJBsRSIIgCIIrtGnThueee46zzjqLK6+8EtM08Xg8/Pe//+WSSy5x27x6Q7NmzVi7dq0IJD8kxCYIgiC4xplnnsmYMWMwTZPMzEw+++wzEUdxxk7UtgWS5CBZiAdJEARBcJXnnnuO3r17M2LECHr37u22OfUOf4EkHiQLEUiCIAiCq2RlZTFp0iS3zai32AKptLQUEIFkIyE2QRAEQajH2ALJRgSShQgkQRAEQajH+AskyUGycDXEppRqC3wMlALlwHla601KqSOBB4EK4DKt9SIXzRQEQRCEOot4kALjtgcpHzhSa3008BpwkXf6PcBw4FzgAZdsEwRBEIQ6jwikwLjqQdJalzu+NgGWKKUaAuVa6+3AdqVUC3esEwRBEIS6jwikwLjei00p1Q94HmgGHA80B3Y6FilTSqVrrUvib50gCIIg1G0kBykwcRFISql2wDsBZo3WWi8ABiilRgGTgGuALMcyqf7iSCk1AZgAMHHiRIYNGxYTu8Hq9pibmxuz9ceDZG9DstsPyd+GZLcfkr8NyW4/JH8bkt1+CNyGkpLK/oft27eTkpIST7PCJtrHIDs7O+i8uAgkrfVm4Bj/6UqpdMfXAmCP1nqPUipVKdUMK+y2LcD6XgBe8H41o26wg9zc3JA7MBlI9jYku/2Q/G1Idvsh+duQ7PZD8rch2e2HwG2w6x/ZdO3a1TeAbaIRz2Pgdoitn1LqYawebMXAeO/0/wBTscTP5S7ZJgiCIAh1HslBCozbSdqzgaMCTP8JGBR/iwRBEAShfpGVlYVhGL7BgtPS0tw2KSFwu5u/IAiCIAgu4vF4fCG1jIwMDMNw2aLEQASSIAiCINRz7DCbhNf2IQJJEARBEOo5IpCqIgJJEARBEOo5tkCSGkj7EIEkCIIgCPUc8SBVRQSSIAiCINRzRCBVRQSSIAiCINRzRCBVRQSSIAiCINRzJAepKiKQBEEQBKGeIx6kqohAEgRBEIR6jgikqohAEgRBEIR6zrBhwxg0aBDnnnuu26YkDG4PVisIgiAIgstkZ2czffp0t81IKBJCICmlxgBPaq1be7+fDfwLKALGaq03uGmfIAiCIAj1C9dDbEqpFOBsYL33eypwDXAMcCtwi2vGCYIgCIJQL3FdIAFjgPeBCu/3/YGlWusSrfV0oI9rlgmCIAiCUC9xVSB5vUejgHcdk5sDOx3fU+JqlCAIgiAI9Z645CAppdoB7wSY9Srwnta6QillT9sBZDmWKQ+wvgnABICJEycybNiwqNrrpLS0lNzc3JitPx4kexuS3X5I/jYku/2Q/G1Idvsh+duQ7PZD8rch2vZnZ2cHnWeYphm1DUWKUuoB4BCs8NpA4P+Aa4GfgKMBhZWkfYlrRgqCIAiCUO9wVSA5UUpprbXyfj4H+CdQjCWQ1rtqnCAIgiAI9YqEEUiCIAiCIAiJQiL0YhMEQRAEQUgoRCAJgiAIgiD4IQJJEARBEATBDxFIgiAIgiAIfohAEgRBEARB8EMEkgNvZe+kRSnVyPvfcNuWmqCUauO2DbVFKdXZ+z9Zj0GO93+y2j8gWW23UUqdpJTq4LYdNUUptZ/bNtSWOnAvbe62DdFAKeWqRqn33fy9NZdGaK0vcNuWmqKUOg04H2vA34e01hvdtSgylFKnAv8AdgNPAzO01hWhf5VYeG+oDwKdgLO01qUumxQR3mNwCfCT1voBt+2JFKVUX+AJ4FfgVq11icsmRYxS6iSsQrmDgZ5a6zXuWhQZSqkTgYnAXuBt4EutdaG7VkWGUmoEcB6wAXhUa73JZZMiQil1NNY5lA88AyzRWhe7a1VkKKVGA+drrUe4bUu99iAppXpjDZZ7qFJqvHdaUnmRvBf034EHsIZpudE7PSnefJRSR2CJu/uAj4HjvUPPJIX9NlrrPUAJ0ATreCTTMTgWuB1LXD+glGroskk1YQhwr9b630A3t42JFKXUmcB44ErgMeBsdy2KDO9981LgBeAOrFEQMpPlGgBQSqUBY4HngJXAZUqpge5aFTHnYA3h9TZwMnCmu+ZEhtdzejqwn+OZ7No5VO88SN43/dHAd8BmrXWx96B8BBzj/W5orRN2x3jbMAb4n3dSqdZ6q1KqKdaYd2O11ltcM7AaHMfgGyBPa73XO70t1g32amBDInthvG04B8vjssp7EV8G/AZcBVyntV7npo2hcByDaUAGcAYwAGgGFABPAtO11mVu2RgKx/7/RWu9wnszPRjoB2wC5gCfaa1XuWdlaPyu40Kt9U7v9OOAQcAD9rWRiDjOoR+BQizv0RRgBdYA5JcCGxPZm+fXhgrgaq31P5VS6cDzWF75J7XW+S6aGRTvy8ytWN66H5VS1wM/A7OxPJHDgcla62UumhkSbxtuAz7XWv/indYeeAM4x819X688SEqpMcAPQEMg1yGGNgLTsd6iSXBxZLchA9iitd7oFUceLO/F6gQXR85jsMkhjnKwXMImcD1wkls2VodfG9aB75w5CGiKJbYvUUp1csvGUPjZv9krIpYCC7TWxwFvYu3/hPTE+Nm/xju5EdAeuA64HCvMM9wF88LC7zreaosjL5lAM631XrdzMILhfwy01nnAt1je4PlYIZ5/AFe4ZWN1+LVhtdZ6NdBBKTXaK+q2Yl3PCelR9d5f3gbygJneyQbQFes++jvW/am7KwaGgaMNm7FeagDwhjZnYoULXSMhL75YoJTKAkYBd2FdyEcrpQ50iKG7gSOVUi2VUk2VUhlu2RqMAG04Ril1IIA3Z6cRUOZddr9Ec28HOwbe2VuAK7TWpwFLsG5MCRem8mvDd1ht6OWd/SMwDyuX6nwsT5LriYZOAhyDoUqpblrrz4H7AbTWHwEdgQNcMzQIAfb/37we4A+BNKCT1roASzjZ10Iin0P2ddDDscjXWO3qnIi5eEHOof211j9geYWf0VqfD3wOpCulPElwDI5XSrUE/g0coJSaivV8bIUlvBORVOBT4HvgSqXUICyP8CCgl9b6LywB3hAS7zrw4mzD5Uqpkx3zHgH6KaU6KaU6KKVaxdu4Oh1i8/amuA74AstDdBTwLyAd66BcCIy0kyGVUndgPdQ+xAqR7Ii/1ZWJpA1KqYuwLo4CoCWW4HA1SbIGx6AVVrLtbK31E27Y7E+YbTgeK8n5GCyxtxHYrbW+xQWTK1GN/Z8BF1D5GGRh5WG8orX+xg2bnYRh/4XAUKx93wfrzflkYKXW+g4XTK5CDa6D2wHtFa6uE6b9J2F57dpjPagnAtu11le5YbM/YV4HI7TW65VSLbXWfyml7sEKUa1wy24bh/2fAouxXmKuA3KxXszGYb3k9AaygGXACKw0gJdcMLkK1bRhLlYbngG+01rvUkrdjDVw/ZfAjfFOmk+YN9too5TqiKVA1wLtgNe01lOBh4C/aa0fwUpmu8+7/H7AocA9WuuLE0QchdsGu9fRflgCaYXWemwCiKNIj8EIrByqWQkkjsJpw2tY4dmHsUTFaK31NQkijqqz/2EqH4MLganA/AQRR+HY/39Y+TrvY7nrB2D1hEwUcRTpdZCOdW9OiPypCK+BV7BykG7HeslJFHEUznk0GesaBuiilJoGFCSIOHLanw38V2utsV6ES7TWb3rnHw+8jhXmPxqYk0DiqLo2vIV1PIYCFd6c2gFYnUcudKNHYZ0TSEqpoxyuxGZa60e01v8HNFFKTdJaf4UVpwWrS3m5UioTK7FzlPdCcZUatMFOpP0GGKi1fjbOJleiBvZXeHvBLANO11o/6YLZlYiwDU9gvbGhtX7D+3tXr60aHgMDWAAM11o/FH+r91GD/Z+ulMrSWv8OXOv2NQA1vhc19ea/3Km1XuqG3TY1OAaZQIbW+m0sb9hTLphdiQjb8BRQqpRqgPUQP1Nr/aALZvsIYX9TpdTFwD3A4QBa6y+BA73LLQauSvBjEKgNX2O96Lf1hspHuXkvqjMCSSnVWCn1NVY8+WSsJMFflFKXeBf5GThVKdVMa12ulDoK+ARYpbXerbUu1VoXuWK8l1q04U8ArfXPbnq+ankMyrXWK7XWu1wx3kst2rBSW139AV9OWNypzTmktTa11r95b0yuUMtzaCeA1rrcBdN91LINBQDaxd6DtbwGdgNol3uu1fI62Ku1znfTAx+G/T9hlYX4yTv9Nu/yG73LJsN1EKwNm4C/ALTLNZzqVA6SUuowrEJ9h2PFMZt5/6/B6oa6G8vLsgR4ESsc8qEbtgYj2duQ7PZD8rdB7HefZG9DstsPyd+GMOzfiyXqZgJtsRKzv3LB1KAkexvqlECyUUo9iRX/fkNZ9RSysAp/XQ28qbXe7KZ94ZDsbUh2+yH52yD2u0+ytyHZ7Yfkb0M19r+uE7isi02ytqHOhNigUjfGN7G6ybbxJnY1Bd4HOgO7VAJ1u/Yn2duQ7PZD8rdB7HefZG9DstsPyd+GMO0vVInZfR9I/jbUSQ8SgFLqSiAH2I7VG+QPrfVsd62KjGRvQ7LbD8nfBrHffZK9DcluPyR/G5LdfkjONiSkcq4NjreBPlh1Of7UWr+R6AfCSbK3Idnth+Rvg9jvPsnehmS3H5K/DcluPyR3G+qyB+lMrLFdEnYso+pI9jYku/2Q/G0Q+90n2duQ7PZD8rch2e2H5GxDnRVIgiAIgiAINaXOhdgEQRAEQRBqiwgkQRAEQRAEP0QgCYIgCIIg+CECSRAEQRAEwQ8RSIIgCIIgCH6kum2AIAhCMJRSXYDV3q+3aq3v8k5/GWugS7TWNarCq5Q6CBgF/KC1/sE7bTIwFuivtda1sV0QhORGBJIgCMnCOKXU3UAmlrCpLQcBt3k//xCF9QmCUIeQOkiCICQsDg/Sn0A34FigK/BfIB/IxkoVuBn4B9AC0MBErfUSpdTtWCLoJeAYrNHELwfmsM8zZfM3YByWB+kR4Gzvus/VWv8ckwYKgpCwSA6SIAjJwFJgFlZYbTzwCbDDO+/vwF3Ab1hCqT8wRSmV5vj9EOBprEEy7we2Ao97530IjAF+dyw/CHgR6AjcHt2mCIKQDIhAEgQhWXgFy6szGHjVMf1k7/9rtNZPAlOwBsU8wLHMo1rrJ7A8UV201ruB6d55i7XW72ittziWv11rfTewF+gS9ZYIgpDwiEASBCFZeAcoBzYAXweYb/r9d7LN+7+Mffe9UPkFzuVTIjNTEIS6gAgkQRCSAq31Tqzw2iVa6wrHrC+8/x9VSl0JjARWAX9Us8rt3v9DlFKjlVINo2qwIAhJjfRiEwQhadBavxtg8mSsZO1/YCVxz8FK0i5VSoVa3S/At8BR3t91iqqxgiAkNdKLTRAEQRAEwQ8JsQmCIAiCIPghAkkQBEEQBMEPEUiCIAiCIAh+iEASBEEQBEHwQwSSIAiCIAiCHyKQBEEQBEEQ/BCBJAiCIAiC4IcIJEEQBCGpMAzjOcMwbonSuvYzDKPQMIwU7/cfDMO4OBrr9q7vf4ZhjI3W+oT4IQJJcB3vDWm7YRgN/KafaxiG9t68NnlvNEd6591uGEapd579d4M7LRAEIZoYhrHGMIwiwzB2GYaxwzCMGYZhXGoYhgfANM1LTdO8K8z1HBdqGdM015mm2dg0zfIo2H27YRhv+K3/JNM0/6+26xbijwgkwVUMw+gCDMEaOPRUx/RrgMeBe4G2wH7Af7HG2bJ513tjs/8ejJfdgiDEnFNM02wCdAbuB24EXo7mBgzDkOG2hKCIQBLc5kLgV6zxtMYCGIbRFLgTuMI0zY9M09xtmmapaZqfmaZ5vXumCoIQb0zTLDBN81PgHGCsYRgHG4Yx2TCMuwEMw2hlGMbnXk/TNsMwfjYMw2MYxutYL1af2R5mwzC6GIZhGoZxkWEY64DvHNOcYinHMIzZhmHsNAxjimEYLbzbOsYwjA1O+2wvlWEYJwI3Aed4t7fQO98XsvPa9R/DMNYahrHFMIzXvPc7HHaMNQxjnWEY+YZh3BzbvSuEQgSS4DYXAm96/04wDKMtMBDIAD520zBBEBIH0zRnAxuwPM5OrvVOb43lbb7JWty8AFiH5Yny9zAfDfQETgiyuQuB8UB7oAx4Mgz7vsTyeNue7b4BFhvn/fsb0A1oDDztt8yRQA9gKHCrYRg9q9u2EBtEIAmu4c0n6gy8Z5rmXGAVcC7QEsg3TbOsmlWM8r412n8dYmyyIAjushFo4TetFEvIdPZ6mn82qx+F/XavZ7ooyPzXTdNcbJrmbuAWrHtNSu1MB+A84FHTNP80TbMQmASM9vNe3WGaZpFpmguBhUAgoSXEARFIgpuMBb4yTTPf+/0t77S/gFZh5Ae8Z5pmM8ffxlgaKwiC62QD2/ymPQSsBL4yDONPwzD+HcZ61kcwfy2QBrQK28rgdPCuz7nuVCzPl81mx+c9WF4mwQUkQU1wBcMwGgKjgBTDMOwbQgOgGbAJ2AucBnzghn2CICQWhmH0xxJIvwAD7Ommae7CCrNdaxjGwVh5RXNM0/wWq/NHIKrzMHVyfN4Py0uVD+wGGjlsSsEK7YW73o1YXnPnusuAPKBjNb8V4ox4kAS3OA0oBw4C+nn/egI/Y8X/bwWeMQzjNMMwGhmGkWYYxkmGYUhPNUGoRxiGkWUYxgjgHeAN0zQX+c0fYRhGd8MwDKAA675S4Z2dh5XrEynnG4ZxkGEYjbA6jHzgLQPwB5BhGMZwwzDSgP9gvdjZ5AFd7HIEAXgb+JdhGF0Nw2jMvpyl6tIJBBcQgSS4xVjgVW8Nks32H1bC4nnAE8A1WDegrVgu74nAJy7ZKwhCfPnMMIxdWNf+zcCjwN8DLLc/8A1QCMwE/mua5vfeefcB//HmKF4XwbZfx+pZuxmrw8hVYPWoAy4HXgJysTxKzl5t73v//2UYxrwA633Fu+6fgNVAMXBlBHYJccSoPpdNEARBEAShfiEeJEEQBEEQBD9EIAmCIAiCIPghAkkQBEEQBMEPEUiCIAiCIAh+1AWBZIb7t3nz5rCXTYS/ZLM3GW0WexPK3mTD1WPi5rlQH7ctba6z2w5KXRBIYVNeXu62CRGRbPZC8tks9saWZLO3JrjVRjf3bX3ctrS5/mzbpl4JJEEQBEEQhHAQgSQIgiAIguCHCCRBEARBEAQ/XBmsVinVFPgaaxyuI7TWix3zUoAXscrHz9VaX+2GjYIgCIIg1F/c8iDtAYYTeKT2EcBGrfUQIFMpNTCulgmCIAiCUO9xRSBprUu11luDzB4EfOX9/CUwOD5WCYIgCIIgWLgSYquG5sBO7+cCoIX/AkqpCcAEgIkTJzJs2LCwVvz2228zZsyYKJkZe0pLS8nNzXXbjIhINpvF3tgSib3Z2dkxtkYQYodhGGEvK4PEJweJKJB2AFnez02Bbf4LaK1fAF7wfg37TNu4cWNS3YRzc3OTyl5IPpvF3tiSbPYKgiDYJGIvthnAcd7PJwDTXbRFEARBEIR6iGsCSSk1FTgeeFEpNU4p9bx31ufAfkqpn4FirfVMt2wUBEEQBKF+4lqITWt9st+kyd7pZcC4eNsjCIIgCIJgk4ghNkEQBEEQBFdJxCRtQRCEmBKoWK1SqiPwX6AJ8JPW+jY3bRQEwV1EIAmCUB+xi9U+5Jj2EHCZ1jp56igIghAzJMQmCEK9w79YrVIqDegCPKKU+k4pNcg14wRBSAjEgyQIggCtgH7AOUAJ8BnQ33+hQEVq3Sre6WbR0Pqy7blz5/o+5+TkVPruz5QpU8Jeb6T2yzkWO0LVaROBJAiCYBWoXam1XgeglCpVSqV6e9X6CFSk1q1imG4W4awv2+7YsaPv85QpUxg5cmRU1htpJW05x9xBBJIgCPUerXWRUuovpVQzoBRo4C+OBEGoX4hAEgShXuItVtsP6OEtVHsTVmgtHZAebIJQzxGBJAhCvSRAsVqAIXE3RBCEhER6sQmCIAiCIPghAkkQBEEQBMEPEUiCIAiCIAh+iEASBEEQBEHwQwSSIAiCIAiCH671YlNKPQAMAtYA47XWpd7pDYH3gCygDDhXa53nlp2CIAiCINQ/XPEgKaX6Atla6yHAMuAsx+yTgMVa66OBycBF8bdQEARBEIT6jFshtkHAV97PXwKDHfNWApnez82B/DjaJQiCIAiC4FqIrTmwyfu5AGjhmLcCOEgptQQwgMP9fxxowMhwqKiocG3gvZrg5kCBNSXZbBZ7Y0sk9ro97pIgCIITtwTSDqwcI4CmwDbHvLHAL1rr25VSZwG3ADc6fxxowMhw8Hg8SXUTToTB+iIl2WwWe2NLstkrCIJg45ZAmgFcA7wGnABMd8wz2BdWy8cSUIIgCFFDKdUU+Bo4CDhCa73YO70z8AdwmD1NEIT6iSs5SFrrBUCeUupnoBfwoXewSIC3gBFKqR+Au4BH3bBREIQ6zR5gOPCB3/QbqPzCJghCPcW1bv5a6+v9Jl3inV4AnBh/iwRBqC94y4psVUr5pimlumKF7Ne5ZZcgCImDawJJEAQhwbgRuB+4PdgCgTqIuJU472bCfn3Z9pQpU3yfc3JyKn2vDZHaL+dY7AiVIykCSRCEeo9SKgdAa73G6VXyJ1AHEbcS0d1MgK8v2+7YsaPv85QpUxg5cmRU1muaYfctAuQccwsZakQQBAH6Ar2UUl8Cw4DnlFIZLtskCIKLiAdJEIR6iVJqKtAP6AE8763sj1JqMvCw1rrYPesEQXAbEUiCINRLtNYnB5k+Ls6mCIKQgEiITRAEQRAEwQ8RSIIgCIIgCH6IQBIEQRAEQfBDBJIgCIIgCIIfIpAEQRAEQRD8EIEkCIIgCILgh3TzFwRBEISQGKzY3BQ63gBZR0JGF2jQEfCAuRdKNsHuJVCo4a9PoXiV2wYLUUAEkiAIgiAEIrUFtP07tJ/AdW90h/b7QcGPsHUm7M0Fsww8DaBBJ2jUCzpeC90ehsKFsPFp2PKmJaCEpEQEkiAIgiA42FNs8th7QP9VYJbA5ld55lbFFeOPq+aXBjQZAG0vgJwnoctdsPZ22PwyUBF7w4Wo4ppAUko9AAwC1gDjtdaljnmjsUbM9gCTtNYzXTFSEARBqFd8o03G32+ycw+w/h7LE1RRTMeWU8L4tQm7frX+1t4G2ddAzhPQ7mJYeRkUzou1+UIUcUUgKaX6Atla6yFKqZuBs4C3vfM6ACOBoVrryIY8FgRBCAOlVFPga+Ag4AhgLTAF655YBvxda73WPQuFeFO81+S6/5o88zFccAI8NtGgVbOHa77C0nxYcxNsfgW6PwV9p8OamyH3segZLcQUt3qxDQK+8n7+EhjsmHcisBf4Win1ulKqcbyNEwShzrMHGA584P1eCpyvtT4KeAC43i3DhPizYYvJUVeavPMdfHyPwWs3e2jZ1IjOyotXwuKTYPUN0OVuOGgKO3fLu38y4FaIrTmwyfu5AGjhmNcWaAUMAy4DJgL3O3+slJqAFYJj4sSJDBs2LKyNVlRUkJubWyvD40lpaWlS2QvJZ7PYG1sisTc7OzvG1uzDG9LfqpSyvxcDG72zS5CEkXrD7N9NTr3JpF0L0C8YdGkfJWHkz8anoOAX6PUxgy83+fwB6NwuRtsSooJbAmkHkOX93BTY5jfve621qZT6FviP/4+11i8AL3i/hi3FPR5PXG/CtSU3Nzep7IXks1nsjS3JZq9SKh24Hbg4yPwqL2duiVY3xXJd2fbPi9K56JHmHNWnhKeu2E5aBThXPWXKvryjnJycSt9ryl+Fy3h8WlsOn5DC2zdto0ensmp/I+dY7Ah1fwpLICml+gDHAp8C2cAarfX6Wtg0A7gGeA04AZjumDedfe7tfsCftdiOIAhCJLwA/FdrvSLQzEAvZ26JQDfFZ13Y9pSfTcY+ZHLB8fDctQ1JTW1UZZmOHTvuW37KFEaOHFnr7QIU7qngtJtNzrmnNV8/atBv/9CeJDnH3KHaHCRvj7J5wCNAF+BW4MnabFRrvQDIU0r9DPQCPlRKPe+d9xuwXin1AzAeeKo22xIEQQgHpdRtwJ9a63fdtkWILf/71eTs20wuPw1evMEgNTW+oa7Mhgaf3WdweE849mqThSslJykRCceDdAfwLWAXgPgcuKm2G9Za+ydBXuKYV+v1C4IghEIpNRXLS93D+/kW4Bel1LHATK31JDftE2LDD/NNzviPycUj4NGJBobhTh5QRgODj+6G0282OeE6k1+ehu4dJScpkQhHIHUAXmGfQCoDGsbMIkEQhDigtT7Zb9JdrhgixI2FK01OnWQyeig8fbV74simQbrBB3fB8deaDLvGZPp/oUMrEUmJQjjd/BcBF3o/X4CVNL0wZhYJgiAIQpTZsMVk+I0mR/aGF6838HgSQ4g0yjD4/H6DJo1gxI0mu4sk3JYohCOQrgXaAQYwFkgDroulUYIgCIIQLQr3WOKoTTN494745xxVR7MmBp8/YLDxLzj/bpOKChFJiUC1Ask7zEd3YIT3b3+t9axYGyYIgiAItcU0TcbdZ7J1B3x2v0GTRokljmz2a2vw6b0GX86Cm18UgZQIhNOL7ULgFKzija2AU7zTBEEQBCGhufd1+GwGfHS3QXbrxBRHNocfZPDqJIP734QPfxCR5DbhJGlPJnAxxteia4ogCIIgRE7QZOvmJ0Cvz2HFJQw8+BXA8iglMqOHGsxeanm9DuoCPbsktqiry4QjkG5gn0BqjpWw/UvMLBIEQRCE2pKeDT3+zxosNu8Vt62JiAcuNZi73OTMW0zmvFD98kJsqFYgaa0rDWeslFqIVS9EEARBEBKQFDjwTSjZBH/+021jIiYt1eDd26HveJOrnjC58wK3LaqfVCuQlFKf+i1/GFZPNkEQBEFIPDrfBo0Pgfn9oaLYbWtqRLuWBq/fDCdcZ3JItwwmjnLbovpHOCG2EX7fi4F/x8AWQRCEiPB2GPlRa73W+70l0ENrPcNdywTXyBoCnSbBHxdB0R9uW1Mrjj/c4MZzTf79UlNOPtKkWwfJR4on4Qikro7P5UCe1ro0RvYIgiBEwqvAaGCt9/sw4E0gJdSPlFJNga+Bg4AjtNaLlVJnA/8CioCxWusNMbNaiA0pTaHHa5D/PmypG/2I7rrY4MtZZVx4j4cfn4SUFBFJ8SKoQFJKnRFiHlrrj2JjkiAIQmiUUqcCp2EVsL1cKXWSd9YhWF7u6tgDDAce8q4vFbgGOBroj5VneUnQXwuJSfdnrP8rL3fXjiiSlmrw5OU7OPGmNjzwFtwk+UhxI5QH6QMCd+83vNNDvqEJgiDEkEOAcVj3oqO9fzZvVvdjrxd8q1LKnrQ/sFRrXQJMV0o9HPTHQmLS6ixoMwZ+GwplO9y2JqrkdCjnkSsMrnrC5MTD4dAe4kWKB6EE0p0EFkiCIAhu8wLwBTAbuBn4Cut+tV1rvboG62sO7HR8lxfAZCKtjeU9yn0SCn5w25qYcOlImPILjLvP6vrfIF1EUqwJKpC01rfHcsNKqQeAQcAaYLx/XpNS6t/AWVprFeDngiDUY7TWm4BNgMcbHmuLV9QopfbTWq+LcJU7gCzH9/JACymlJgATACZOnMiwYcMoLS0lNzc3ws3VHre2G69tz507N+D0nJycSvNMEwacP5/1f2Xw+HXdaZA2JeR6I7F7ypR968rJyan0vTZEuu9KS0vZuHEjd13oYej1rbnh6d3ccE5hVGypbrt1+RwDyM7ODjovnG7+7YHbgd5AhneyqbU+rKYGKaX6Atla6yFKqZuBs4C3HfObeLcnCIIQFKXUlcD97Ls3geVJCqcDipMVQE+lVDqggN8CLaS1fgHLe2Vvh9zc3JA32Vjh1nbjte2OHTsGnD5lyhRGjhy5b0Lr0dDjZFh4FKM+nVnteiOppO20ocp2a0Gk1bzt/Z2dDY9fZTLh4Sacf1IW/XvG1otU18+x6gjnJvIScAJW7lEZVg2kHbXc7iAslzjAl8DfcQgk4J/A08BTtdyOIAh1mzuwkrJ/wro/hY1SairQD+gBPA88DvzgXd/YKNooxIq0VpDzOOQ+AbuqF0d1gb+fDB/8ABc9YKJfhPQ0CbXFinAE0iDgPuAmrF4fZwB/1XK7zbHc4wAFQAt7hrf7bW+t9d2OBMpKBHJzh0NFRYVr7sKa4KZ7s6Ykm81ib2yJxN4avi2uA57XWj8b6Q+11icHmPxuTYwQXKLbE1BWAGvrz+AOhmHw3HXQ60KTB96CW0TKx4xwBFI6sBrLg9QP2IXl4anNGbmDffH+psA2x7yrqcZzFMjNHQ4ej8d1l10kJIKLMVKSzWaxN7bEwd7fgVuUUh2A7d5pptb6sVhuVEgAWoyANqPht2Ohoshta+LKfm0NHrgUrn7K5Myj4SAZ0DYmeMJYZg3QCism/wBwHbC+ltudARzn/XwCMN0xrzvwH6XUl8D+3hwlQRCEQIwG2mH1ZHvY8SfUZVKaQPenYdNLUPCj29a4wqUj4YiDrFBbRYV0OI8F4XiQRgElwFTgP1gem3tqs1Gt9QKlVJ5S6mcsF/nDSqnntdaXaK19ZbCUUlprXattCYJQpxmPlCOpf3S5G4w0WHOj25a4hsdj8OIN0OfvJs9NgctPd9uiukc4AukfwOta67lYb2tRQWt9vd+kKlVrpYu/IAih0FpPdtsGIb4s39gM2l8Oy86tcwUhI6XHfgY3nQ+TXjA5bQh0aCWhtmgSjkC6CrhSKfUH8AbwptZ6TUytEgRBCAOl1J8BJpta65y4GyPEHiOVZ77qA9v+Z423VpNVGHVLRPz7PHjnW7jycZMP765bbXObcHKQDsaqql0K3AWs8obGBEEQ3KYN0Nr7tx/QBatopFAX6fBPNu9oBKsmum1JwtAg3eCF6w0++gk+nyHR5mhSrUDSWv+utb4DOBHwjgTIoJhaJQiCEAZa68Za6yZa6yZAI6xaRi+5a5UQExp0hs63MXrQH7A30kLpdZshfQ3GnwwTHzfZXSQiKVqEU0n7aqxK10dgCapVwFuxNUsQBKF6lFKHOr6mYpUlORerXIhQl8h5EopWcuphG/g/t21JQB641ODAC0zunGzywGUSaosG4YTYHsUa6fpZYKDWen+t9W2xNUuoz0yePNltE4TkQQNzvH8zgcuBP1y1SIg+LUdCyxGw8nJSU8RDEohWzQwevtzg0fdg0SrZR9EgnCTt4cBXWuuAgzcKQrRZs2aN2yYIycNr7OvmX45Vt+1F16wRoo8nE7o9DptehF2/um1NQjP2RHjlC7j8MZMfn7RKAQg1p1qBpLX+XzwMSRQmT57MuHHj3DZDEIQw0FqPU0qlAAd4J/1R05c5pZQHeAXIwRo54GKt9bLoWCrUmP1ugZSGsGaS25YkPIZh8Oy10G+8yWvTYNxJbluU3IQTYqtXiPdCEJIHpVRPYCmw2Pv3u3daTegHNNBaDwEmAddExUih5jTqBdlXw+p/Q9n2ahcXoFdXg2tGwfX/NfmrQEJttUEEkiAIycwzQHvgbe9fe6oZyzEEGwBDKWVgDaidHxULhZrT/WkrrJYnadmRcMtYg4YN4KYXRCDVhqAhNqVUFrBbco8EQUhgFDBJa/00gFJqInBvDdeVj1XvbRmQAQyusjGlJgATACZOnMiwYcMoLS0lNze3hpusOW5tN17b/ue9mqe+7MtjF/5Ml9af+Kbn5OQwZcqUmG47ENHcbiT7bu7cueTk5DB37txqlz3ssMN8n289vwETHmvOiMO3cGj30hrZWdfPMSDkYNqhcpC2A6OVUl8D84DztNYzo2ybIAhJSoLk620DjlNKTfV+Hwb8VcN1HQ+Uaa17KKUU8AhwjnMBrfULwAveryZYD7tQN9lY4dZ247Ht7btMnpjigS2P8c+Lb6g0b8qUKYwcOTJm2w5GNLdrmuF7djp27Bj2tp3rvaiDyUfTTW5/vRWznzdISYk8Ybsun2PhECrEZnj/UrCq0zaMh0GCICQHCZKv9xJwKrDC+3cKNe/FZrBPXOUDTWttnVAj/vOiCWYZrLvTbVOSFsMweOpqgyVr4NlP3LYmOamuF5sZ5LMgCEIi8DqwBctzBDAN+LqG6/oaGKeU+hFogCRpu8Lc5SbPTgH+vBbKC902J6nJyTaYdB7852WTs46Bdi2l238kVCeQHgYKscTRS0qp3d7ppta6b0wtEwRBqJ5vgHe01mcDKKXuwhI6B4T8VQC01mX4hdSE+FJebnLZIybHHQZf/1SzwWiFytx4Lrz+FVz/rMnr/xGBFAnVCaROjs9do7lhpdQDWGO6rQHGa61LvdNPAf6DlSw5V2v9z2huV6gbJEj+i+A+2Vj3EJu1QEd3TBFqy4ufw8JVsGiyQY/H3LambpDRwODpq+Gk600uHmFydD8RSeESVCBprWNWAkAp1RfI1loPUUrdjDXW29ve2QuBwVrrMqXU20oppbXWsbJFSE4SJP9FcJ8/geuUUrlYOUTXeqcJScaW7SaTnje5YQwc0Eke4tHkxAEGZx5teecWvALpabJ/wyGoCFJKDVZKXeY3zVBKXaqUqtL9NUIGAV95P3+Jozut1nqd19UNUAJU1HJbgiDUXR4AegBfAJ97P9/nqkVCjbjxOZPmTeCmC+ThHQsem2iwbgs89p7bliQPoUJsjwILnBO01qbX+3MxVv2RmtIc2OT9XAC08F9AKdUfaKO1nhdgXpVaJOFQUVFRbV2FnTt3ulb3wR83a1DUlGjYHM4xiNZxSrZ9nEj2hnMMIrG3Jl16tdavK6XWAiO8kz7XWv8U8YoEV/l5ocnk/8EXDxg0bCACKRZ0amtw+zi47VWT0UOhczvZz9URSiD1Ap4PMH0OcEEtt7sDyPJ+bopVy8SHUqoj8DhweqAfB6pFEg4ej6fam3BWVpbrtRdsEqEORKREw+ZwjkE4y4STp5Rs+zhe9oaz78I5BvGw1yuIRBS5hGHU8kFrpMIhc6FoJcMHnRkdo+op1R4LIxUO0XQ5dhUsrX5fT5kyhY4dO0ZUt6kuESrPqAQ4LMD0w7zzasMM4Djv5xOA6fYMpVQT4B3gEq31llpuR6jHSJ5SzZF9J8SN7Kshowuskv44Mccsg5VXQKvToMWIahev74TyIH0HXGIVlGWad9rxwCXAx7XZqNZ6gVIqTyn1M7AOeFgp9bzW+hLgaqwec097t32b1vrH2mxPEARBSEAa7Af73Qprb4eSDW5bUz/YOR02vwI5T8CO76Bij9sWJSyhBNINwBDgMuBS7zQDqyjbjbXdsNb6er9Jl3in3wXcVdv1xxLpYi4IghAFcp6A4lWw8Um3LalfrP43qN9hv1tgzSS3rUlYgobYtNZ/An2Ae7B6mv0PS7j08c6rt0j4oeZMnjzZbRMEQUgEWo6ElqfCisut0I8QP8r+gtU3Qva/oNHBbluTsIQsFKm1zgNucU5TSh2slJqotb41ppYJdRIRl4IgkNIYcp6ETS/ALhkD3RXyJkPbsdD9v/Db0choYlWprpI2AEqpA7FK8I8CDvROFoEkCEKdQil1DNZLoQd4Umtdq3xLIQid7wAjTcI7brPiMjh0PrT7B2x+ofrl6xlBBZJSan8sQXQOVpd/A0tifoE1QKQgCEKdQSnVEKsS90la69r21BWC0fgw6HAlLB8LZTvctqZ+U7QMNjwAXe+Dvz6F0s1uW5RQhOrmvxy4E6uI4zPAhVgi6SWttYwiKAhCXWMgUAR8ppT6WCnVzm2D6hxGKuz/Amz/Gra+Xf3yQuxZdx+U5EHO425bknBUF2KrAH7E6vK/PPbmCIIguEZboDtwBFadttvZ14MXCFzF363q5m5WVXdue8qUKWH/7sNZObw7c3+e+vtftG0a/u+c5OTkRLTNaBHN7UZy3KZMmRLzNi9e/xc3v3s2N/2jCwO65/mm29uty+d3qEK2oQTSlewLsY3GKg5pAv2VUtO11n9F00hBEASX2QFM11qXKKW+BaokyASq4u9WNXY3q8A7t92xY8fwfpTRDQ79DdbeyIQLH6/xtqdMmcLIkSNr/PtE2G4klak7duwYnzZ3f4573z0J5g6D8l3Avja7UUk7EUY5CNXN/xmt9dFAJ+AawB4T7WZAApWCINQ15gA9lVIG0A+o1+VMoothhdb2LIbcp9w2RgjE6hvBSIEuMtazTbW92LTWm4AngCe8Y6SN8v4JgiDUGbTW+Uqpj7HSCkxgvMsm1R3aXQxZR8J8BZS7bY0QiPICWHklHPQB5L8HBTK8YVjd/G201huAR71/gpD0vPfee/zrX/9y2wwhQdBaP4PVKUWIFunZ0PUBWH+f5UESEpe/Poat78P+L8K8fm5b4zqherEJQp1n/fr1bpsgCHWb/Z+HvestgSQkPquuhNRm0PlOty1xHRFIgiAIQmxoOw6aD4M/xoMppaWSgtKtsOqfkH01S3Obu22Nq4hAEgRBEKJPekfo9iisfwAK57ptjRAJW9+Bv6bwxP/6gaeR29a4hggkQRAEIfoc8CLsXQfr7nbbEqEmrLyM3XtTocu9blviGhElaUcTpdQDwCBgDTBea13qnZ4CvAjsD8zVWl/tlo2CIAhCDWh/GTQ9BhYMlNBaAAzDcNuE6indymXDFvFA0ZV8O9dk6GFJYHOUccWDpJTqC2RrrYcAy4CzHLNHABu98zKVUgPdsFEQBEGoAQ0PgK4PwtrbYfcCt60RasGgAzZD3huMvddk+674F4t0G7c8SIOAr7yfvwT+DrztmPeFY95gYGZtN1hSUsL27dv5448/Qi63YWM+P86wRlVxng7OQqKr1+Xz3S+BR16pTcXRCr+fesq28vuqXVRUhP+byrZUv3x15pqObX/z1Uccd/wZIZdvmLKVuUt2BZ2/bGU+n361LOQ6lq7M55Np+5Zx2mx//n1FPh/8b1nlg+THkj/yee+L0NvasnV7tcuE2sfhEvQYBjpGIX7fOnMr38629m+wY2eGOF/CZcHv+Uz+oOp+ca76t2X5LF26nJSU4G+WeXl5tG7dmvT09NobJSQ+Rir0+D8onA8bHnLbGiEarLqSlD7nc/mjJm/fVr+8SG4JpObAJu/nAqwBcZ3zdgaZBwQeD6k6/vzzTyZPnszkyZOrXfblF6svg/Laq/WzVMpTj9xc63W8+2b1++69MJZ5/63ql/ng7eqXefONydUuUx+Z8n71++7jd6tf5qeffqJbt27VLuf2sAJCFNjvdmh4IMw/jMBSX0g6ynfy2s0Gf/unyYiBJucdX39EklsCaQeQ5f3cFNgW5jwg8HhI1dG6dWt++ukn2rZtG3K59XkVFOze972S98BvS6E8C++/+TRnnzcx5Lbef/Npzjw38DIVFTD1/Qc4+ewbq850bPeDt5/mrDHWOoLdjj5862lOH115O/7eh4/feZrTRoW2N5zb3befPMDQ0wLYHCGhPF3BPCThenqcy3VusZW121qHXP6LD59m+JnB980XHzzNyWeF3ndTvcuE8u588eHTnHTGvvUE8hDN+uoB+g8LfU58+cnTnHhacHuqmx9o24FSJjLS4eh+BqmpoT1IAwYMEA9SfaDp0dDpRlg+DopllJa6xNH9DG481+SyR02O6AU52fVDJLklkGZgje/2GnACMN1v3nHAT955r0Zjg+np6XTr1q3at9QDDojG1mDPVsWo4QeGXOb3Oa0YPSL4MvqH5px7auh1/DG/FeeODL3MyvmtuPD00Mus/q0V484KvUw4LJ7RnPFRWE+8yM1twvBqzoktf7Ti8nODt2nLH624IsR8gK1hLJO/shVXXRB6mWsWNOdfY0MvU7CmFf8aF3yZ6uZD+KHi6pJNMzMzRRzVB1JbQI/XYOvbsPVNt60RYsCdFxl8N89kzB0mvzwD6Wl1XyS5kqSttV4A5CmlfgZ6AR8qpZ73zv4c2M87r1hrXev8IzcYN26c2yYIQo0xDCOsv7qIUmqMUmqr23YkDwYcMBkq9sLK0J5JIXlJSzV4+zaD5evh5hfrR8K2a938tdbX+026xDu9DBgXd4MEQaj3eMuMnA3IGDThkn0NND8OFh4J5TurX15IWrp1MHjhOhh9h8mQPianHlk3X5JspFCkIAjCPsYA7yMZxmExfZEJXe+FP6+DwnlumyPEgXOGGlx+Goy912TNprrtSXLNgyQIgpBIeL1Ho4DTgGuDLFOlB21paSm5ublxs9PGre3a256/ZBNn3tyKwT22cP11J2AYJ8Rl2zk5OUyZMiUu20qE7bq5bXu7n376aaXpx3bxMC1zEMOugvvGzCA9dd/7xGGHHRaVbcfr/A6VlywCSYgqnTp1ctsEQagp5wPvaa0rlFIBFwjUgzY3N9eVEgVubRdgzdpc/vlsW1o0hemTezL95cK4bXvKlCmMHDkybttze7tubjvkdht0gUNmc/a/82DFxb7JtakF6MTN89tGQmxCVBk1apTbJghCTTkI/r+9O4+SqjzzOP6tBgFZGldAAVEWN1xQn1EEUeIJLseYVoM4aAaXOBAjiY4JLoMaFTc8apS4BEajohwXcGlHjXGLCzJxfDCgIig6Koooi3YjNkLTfeePe5uURUE3dFXdW12/zzl9uqpu1X2feuvWW89973vvyygzexboZ2aT4g4oqSZMK2fOh/DY1SmoK1xyJAmy5hNYcCp0HQU7/TLuaPJCPUgiIoC7r7/AlJm5u/8mzniS6q6nAu75a3sevzrFnr1a9iBdaUTVC/DxJdD7FvhuHqx8Le6Icko9SCIlZtddd407hMRz9+zH2ErczLcDfnVzwIUjvqViiJIjARbfBMunw94zoF3jV8wvJkqQREqMrtElW+LDzwNOHB8wfCiMrfiu0edLCfngbFi9EPpXUtWCJrVVgiQiIpu0vCrg2HEBu/eEP1+Uyjr1jJSwYA28dxKUdeBnlwWsrW0ZSZISJBER2aia7wMq/jMglYLKa1O0a6vsSLKoXQrzjuOtD+DM6wLqmzo5ZoIpQRIRkaxq1wWcckXAws/hmRtS7LCNkiPZhJr5PHldikdfhQvvDHJ2yn9clCCJtDAahC3ZNHV+vYY59urrA34xMeDVufDXG1P07aHkSBp3+IAy1swZzk0P1VHWa3xRz+Wo0/xFGlFsCYcGYUtzBUHA2FsCHvlbmBwdsHvyf8wkQVY8Hg7c3uOecH6+L26PO6ItogRJpBFKOKTUnD8p4O6n4YlrUhwxQMmRbIGlU6F1J+gzCYI6WPKnuCPabEqQRETkn3rfzB1PhFfJPnagkiNphi9uB1pB36gHqciSpFgSJDObCAwCPgHOcvfatGXHA5cCtcBsdz8vjhhFmqrYDsGJZFcGfe+ArqN4dEKK4wcrOZIc+CKasafv7VC2NSz+Q7zxbIaCD9I2s/2B7u4+BFgADM94ylxgsLsfBnSxjc0aKZIQOgQnRS+1FewxFbqcBvN+yk8PU3IkOfTFJFh4Dux2A/S6Ku5omiyOHqRBwHPR7WeBM4EHGxa6+6K0564F6gsXWulSL4iUOjM7GLiVsPd6MTAqvXe7xWrVCfaaAR0PgnePgZWvxx2RtERfToG6atj9PtiqK3x0btwRNSqOBGlbYEl0uxrYLtuTzOxfgC7u/laWZaOB0QBjx45l2LBhTSq4traWxYsXb0nMebFy5cpNxrPzzjs3Gm9j62jqc4YNG5aTuklaHTemUPHm6nOqr69vsfXbvXv3PEfTqM+AI919tZldB1QAM2KOKb/a9ID+ldB6O3h7CNTMjzsiacmWPQy1K2Cv6dC2J9/WBHRqn9zeyrwlSGbWDXgoy6LngPLodmfg6yyv7QHcApyYbd3uPgWYEt1t8pWoFi9enIRGeL3y8vJNxjNy5MhG421sHU19Tq4krY4bU6h4c/U5lZWVqX7zxN2XpN1t+b3XnQbC3o/C2i9g7uDwv0i+Vb0Acw+HfZ7i0HMCKq+FPt2TmSTlLUFy9y+BoZmPm9kA4AJgKnA08HrG8k6EidUYd1+ar/hERLIxs17AUcDVWZZt0HsdV6/p5pZbWVkJQBDA8+/swuQX+3Nwn68475ivaNfmzh88t7H1ppfdsN5C6dOnT8HLjLPcOMvOZ7lfr5rP9ZUdGHBmB3573D84cLdlG5Q9e/bsLVr3QQcd1OTnbmoHruCH2Nx9jpl9ZWavAYuAGwHMbLK7jwHOB3YDbovGZ//e3V8pdJyFkItxPxo7JJI7ZlYO3A+ckW38Ubbe67h6yTa33B49eoRnEfW9HbocC59ezqyXJzLr7g074RubIiK97B49emxe4M1UWVlJRUVFQcuMs9w4y857uak20Pc2rpxxJnx2DXx6FQ0dt80pO1dTnMRymr+7j8vy2Jjo/wRgQsGDikEuzn7SGVQiuWFmrQl7r6909/fjjifnOhwAe94PrbcPB2NXvRR3RFLqgrWwcDRUz4S+t0H5EPjgDFjzWdyRAZqLTaQg1NNXFEYChwCXmdnLZnZK3AHlwrp1Adc9EMCAWfD9p/DWACVHkixLp8KcQ6D1NnDgXOjybyRhnltdSVukAHLV09ezZ8+crEc25O73Ex5eazHmLAwnnF2wCPj4d0U7J5aUgJr5MOdQ6PV72P1uJjy2HNruAmsWNf7aPFEPkkgRGTFiRNwhSBGoXhVw3q312OiAbTrCO/emlBxJ8gVr4ZPxMGcwK1ZtDQe9Cz3GhWOVYqAEqQTo8I5IaVi3LmByZcDupwU88je475IUL/whRe+dk3katUhWq97kpp+/Bouugp7j4aC3YfvCD1JXglQCNJBbpGWrrw945KWAfc8IOO+PAWccA+9PS3HaUSlSKSVHUnxatwrg8xth9l6wclZ4tff9Z0LnoQWLQQmSiEiRql0XMO25MDE6dULAofvAB9NSTDynjPIOSoykBVi7BD44C946AGqXw34vwn6vwLbH5L1oJUgiInmWSqWa/NcUy6oC7niyA71PCThrYsCh/eH9B1L8+eIyduna/MSosRhnz569WfGKNFvNu/DeCfCPg8NEaZ+n4cC3odtoKGuflyJ1FpuISBGoqwt4cTbc+2zAo69Ax3YdGFMBvz4pxU47KFGRErFqNsz/GWy9B+z8a+h9I+w2EZY9CF/eA6vezFlRSpBEEkKD6SVTXV3A/8yDGS8HTH8ZvlgOQw+Aey5OMbDfUnrvWhzz3Ink3Or34aOx4VlvXUZCt7NhpzFQ8wHXTA0YP6r5Ow1KkEQSQoPpBWDpNwEvvQV/+XvAM3+H5dVwQD8498QUpw2DXt3Chj+G6d9EkqeuGpb8Kfxr3x92HMmiry4BlCCJiBS3dr2h0yFQPgg6H0bXioA2W8Hh+8Olo1L8ZFByZzsXSZSaefDppUweNz4nq1OCJCJSCK06Q/s9of1e0H4f6LgfdBgAW20Pdath1f/Ciqd4fvL+DN4Xtm6rpEgkTkqQREQiZjYRGAR8Apzl7rXNWV/VtwHHjAtg4Few1Q7hg+uqwz3dVXNh2XT49s3wDJ1gHQA/tkub9R5EJDeUIImIAGa2P9Dd3YeY2XhgOPBgc9ZZ3gGGDoA3KsfD6g+hZgHUfpmLcEUkz3QdJBGR0CDguej2s8Dg5q6wrCzF9b8sgy/vguqXlRyJFJGW0IPU5AP13bsX1ymxxRYvFF/Mije/iizebYEl0e1qYLvMJ5jZaGB0dHeMu09pynsMgiBXMa63uXWb6xjy8Z6SXrbec+mUDS0jQRIRyYUqoDy63Rn4OvMJ7j4FmFLAmEQkJjrEJiISmgX8OLp9NPB6jLGISMyUIImIAO4+B/jKzF4D+gOPxhuRiMQpFfcxPhEREZGkUQ+SiIiISAYlSCIiIiIZSuYstlxfITefzGxX4E1gXvTQye6+LL6IsjOzzsDzwN7AQHd/18xOBv4DWA2c7u6fxxljuo3EuxBomPbzGnd/PrYAM5jZwcCtQC1hjKOAE0hu/WaL9z0SWr+5YmYjgUnuvmN0P+/fATPrCjxOWNd1wGnuvsTMDgNuAOqBc9z9nRyXu8Fn7O61BXrPG3x/o8cLUXbBfj/ibFfjanOybc9AH/K4LTdFSfQgpV8hF1hAeIXcpHvF3YdGf4lLjiI1wHHADAAzaw1cAAwFLgcuiy2y7H4Qb6Q6rZ6T9uP9GXCkux9O2DBXkOz6zRZvkuu32cysFXAy4Xsv5HdgOXCYux8BTAV+ET1+DeE2fiowMQ/lbvAZF/A9b/D9LUTZMfx+xNmuxtXmZNue870tN6pUepAyr5B7Js2cQqAABkdn07wGjHf3xI2mj/ailplZw0P9gPnuvhZ43cxujC24LLLEC9DRzF4h3Fsa6+4bXPsmLu6+JO3uWmAPkl2/mfHWk+D6zZGRwHTgt9H9gnwH3L0u7W4nYJ6ZbQ3Uufs3wDdmtsGFLnNQbrbPuFDvOdv3txBlF/T3I852Na42J8v2/BFhopa3bbkpSqIHifAKuSuj21mvkJswS4C+wOFAF+CkeMNpsvR6BmgVVyCbYXC01/IscGXcwWRjZr2Ao4CZFEH9psX73xRB/W6pqPdoBPBw2sMF+w6Y2QAzewMYC7yVpex1ZtYmT2Wnf8Zxfu8LUXbcvx8Fr9842pyM7XkWBdqWN6VUepCqaOQKuUni7muANQBm9hgwkOK4JksV/6xnCI8lJ5q7r4huzgDOjjOWbMysHLgfOIOwcUp0/abHG+0JJ7p+m8LMugEPZVl0D/CIu9en7e1XkcPPaBNl/2t03aZDzGwEcAnhoZD0sltHe/45LZfwEND6z9jMqijce86czC6nZW9Eehlx/H6klw95/t7H1eZkbM/jydG23BylkiDNImw8plIEV8g1s07u/m10dwgwP854NsNCYK8o0zfg7Zjj2aQozlSUkA4BPow5pB+Ixh48BFzp7u+b2VYkuH6zxJvo+m2q6Ed5aObj0cDdA8zs50A/M5tEeKgtZ5/RJspO35uuBmrcvcbMWpvZNoSHKbb4h3wT5bYGniT6jKOHc/q931jZG1GINifu34+CtatxtTlm1iYtAaoGVgE52ZaboyQSJHefY2YNV8hdBCRq7EYWh5nZ1YR7ah+TvMG465nZM8AAwmPVk4FbgJeB74HT44prYzLifQIYYWbfEfbYnRVfZFmNBA4BLjOzy4A7SXb9Zov3wgTXb7O4+0UNt83M3f030e1byP9nNCAaD1IXldNQt5cCzwAB8Ks8lLvBZ+zuDxfoPf/g+2tmk9393nyXHcfvR4ztalxtTrbtuR/53ZYbpStpi4iIiGQolUHaIiIiIk2mBElEREQkgxIkERERkQxKkEREREQyKEESERERyVASp/lL8kUT9H4MPO3uP4k5HBEpYWb2CbCDu3eMOxaJj3qQRERERDKoB0kSx8wuBM4HdgCWAv/l7ldGy44E7gI6AvcBvwPuc/czYglWRBLNzB4hnM9yJ3dfZmY3AOOAi9hIO5Px+iuA3wMnu/sMM3sX6O/uqWj5JcDoaD2zgHPc/f/y/sYk79SDJEn0GTCBsPF6G7jCzAabWVtgGrBjtHxQbBGKSLGYRjin2InR/eGE0zdlbWc2Z8VmdjpwLfAGcD2wHzA9J1FL7NSDJEnUhXCPbdu0x/YlnJ+nGzDN3f9oZguA52KIT0SKx18I5/Iabmazgd0IJ0PdWDuzOXOtNYyXPCX6A+hmZtu5e6InRZfGqQdJkqYDcDNhMnQC4d4ZQLu052h+HBFpkmgS1OnAjwgPhQWEvUqNtTMNGmawb+hQ2CbLc04DhkV/RxPOoylFTgmSJE0dYQPWlrC3KP2MtgXAl0CFmZ0LXFHw6ESkGE0jTHD+HZgJrGDj7UymT6L/w83sYqB72rKnov+nAz2BI4DL3P37nEUusVGCJEnzPXAhYcN1MWmH0Nx9DeGe2opo2ZxoUVVBIxSRYjMT+BRIAQ+4+yo20s5k8SjwPHAU4Rij9QOw3f2+6PX9CGe+Hwm8mof4JQapINDRCikeZlZB2MitBi4gbLSOd/enNvlCERGRzaBB2lJsdgEuBzoRdn2PVXIkIiK5ph4kERERkQwagyQiIiKSQQmSiIiISAYlSCIiIiIZlCCJiIiIZFCCJCIiIpJBCZKIiIhIhv8HAFgFi38qRO0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_residuals_analysis(model_es.residuals(series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residual analysis also reflects an improved performance in that we now have a distribution of the residuals centred at value 0, and the ACF values, although not insignificant, have lower magnitudes.\n",
    "\n",
    "## Probabilistic Forecasts\n",
    "\n",
    "Some models can produce probabilistic forecasts. At the time of writing, this is the case for `RNNModel`, `TCNModel`, `ARIMA` and `ExponentialSmoothing`. For `RNNModel` and `TCNModel` (as well as other neural network based models), one has to specify a `LikelihoodModel` (e.g. `darts.utils.likelihood_models.GaussianLikelihoodModel` for Gaussian likelihood) - we refer the readers to notebooks 09 and 10 for examples.\n",
    "\n",
    "For `ARIMA` and `ExponentialSmoothing`, one can simply specify a `num_samples` parameter to the `predict()` function. The returned `TimeSeries` will then be composed of `num_samples` samples describing the distribution of the time series' values, which can for instance be used to obtain quantiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_es = ExponentialSmoothing()\n",
    "model_es.fit(train)\n",
    "probabilistic_forecast = model_es.predict(len(val), num_samples=500)\n",
    "\n",
    "series.plot(label='actual')\n",
    "probabilistic_forecast.plot(label='probabilistic forecast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `TimeSeries.plot()` shows the median as well as the 5th and 95th percentiles (of the marginal distributions, if the `TimeSeries` is multivariate). It is possible to control this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilistic_forecast.plot(low_quantile=0.01, high_quantile=0.99, label='1-99th percentiles')\n",
    "probabilistic_forecast.plot(low_quantile=0.2, high_quantile=0.8, label='20-80th percentiles')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembling several predictions\n",
    "*Ensembling* is about combining the forecasts produced by several models, in order to obtain a final -- and hopefully better forecast.\n",
    "\n",
    "For instance, in our example of a \"less naive\" model above, we manually combined a naive seasonal model with a naive drift model. Here, we will try to find such combinations in an automated way, first, using direclty a `RegressionModel`, and then, using the convenient `RegressionEnsembleModel`.\n",
    "\n",
    "If you are in a hurry, jump to the **`RegressionEnsembleModel` approach**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `RegressionModel` approach\n",
    "A regression model is a model that predicts the target time series using some lags of this series (and possibly other series). It is also possible to use this approach to combine the forecasts of several models into one final forecast.\n",
    "\n",
    "Here, we will first compute the historical predictions from two naive seasonal models (with 6 and 12 months seasonality), and naive drift model. To compute the historical forecasts, we can simply reuse the `historical_forecasts()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74372ea3baea43d2a07f1d93a5485279",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3dd85f578a544fc8aaf746ffd226f0d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57794dc01c0f473991bdbef6fc0d4db7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = [NaiveSeasonal(6), NaiveSeasonal(12), NaiveDrift()]\n",
    "\n",
    "model_predictions = [m.historical_forecasts(series, \n",
    "                                            start=pd.Timestamp('19561201'), \n",
    "                                            forecast_horizon=12, \n",
    "                                            stride=12, \n",
    "                                            last_points_only=False,\n",
    "                                            verbose=True)\n",
    "                     for m in models]\n",
    "\n",
    "model_predictions = [reduce((lambda a, b: a.append(b)), model_pred) for model_pred in model_predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predictions_stacked = model_predictions[0]\n",
    "for model_prediction in model_predictions[1:]:\n",
    "    model_predictions_stacked = model_predictions_stacked.stack(model_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the historical forecasts *that we would have obtained* from a couple of models, we can train a `RegressionModel`, in order to learn in a supervised way how to best combine the features time series (our 3 forecasts) into the target series that we are trying to predict.\n",
    "\n",
    "By default the `RegressionModel` will fit a linear regression for predicting the target series from some features series. If you want something different than linear regression, `RegressionModel` can wrap around any scikit-learn regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cc6594a407f4ea3b5c58c1bef2b6d9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/33 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" We build the regression model, and tell it to use the current predictions\n",
    "\"\"\"\n",
    "regr_model = RegressionModel(lags=None, lags_future_covariates=[0])\n",
    "\n",
    "\"\"\" Our target series is what we want to predict (the actual data)\n",
    "    It has to have the same time index as the features series:\n",
    "\"\"\"\n",
    "series_target = series.slice_intersect(model_predictions[0])\n",
    "\n",
    "\"\"\" Here we backtest our regression model\n",
    "\"\"\"\n",
    "ensemble_pred = regr_model.historical_forecasts(\n",
    "    series=series_target, future_covariates=model_predictions_stacked, \n",
    "    start=pd.Timestamp('19580101'), forecast_horizon=3, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's see how good the regression performs, compared to the original forecasts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression coefficients for the individual models:\n",
      "Learned coefficient for Naive seasonal model, with K=6: -0.01\n",
      "Learned coefficient for Naive seasonal model, with K=12: 1.09\n",
      "Learned coefficient for Naive drift model: 0.10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2,figsize=(12,6))\n",
    "ax = ax.ravel()\n",
    "\n",
    "for i, m in enumerate(models):\n",
    "    series.plot(label='actual', ax=ax[i])\n",
    "    model_predictions[i].plot(label=str(m), ax=ax[i])\n",
    "    \n",
    "    # intersect last part, to compare all the methods over the duration of the ensemble forecast\n",
    "    model_pred = model_predictions[i].slice_intersect(ensemble_pred)\n",
    "       \n",
    "    mape_model = mape(series, model_pred)\n",
    "    ax[i].set_title('\\nMAPE: {:.2f}%'.format(mape_model))\n",
    "    ax[i].legend()\n",
    "\n",
    "series.plot(label='actual', ax=ax[3])\n",
    "ensemble_pred.plot(label='Ensemble', ax=ax[3])\n",
    "ax[3].set_title('\\nMAPE, ensemble: {:.2f}%'.format(mape(series, ensemble_pred)))\n",
    "ax[3].legend()\n",
    "\n",
    "print('\\nRegression coefficients for the individual models:')\n",
    "for i, m in enumerate(models):\n",
    "    print('Learned coefficient for {}: {:.2f}'.format(m, regr_model.model.coef_[i]))\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's quite nice: by just combining 3 naive models (two seasonal repetitions and a linear trend) using a linear regression, we get a decent-looking ensemble model, which is better than any of the sub-model, with a MAPE of 6.03%.\n",
    "\n",
    "A couple of interesting things to observe:\n",
    "* Note how the seasonal model with `K=6` and the naive drift model have an incorrect phase compared to the original signal (due to the original signal having a true seasonality of 12). Despite this, the ensembling is able to learn to ignore the seasonal model with `K=6` (by assigning a coefficient of 0), and learns a coefficient of only 0.09 for the drift model.\n",
    "* Note how the regression (ensemble) forecast starts off 12 months after the individual models forecasts -- that is because the regression model needs 12 data points to fit the weights coefficients of the linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `RegressionEnsembleModel` approach\n",
    "\n",
    "Wouldn't it be great if there would be a way to get an ensemble prediction in fewer lines of code? \n",
    "\n",
    "Actually, there is: using the `RegressionEnsembleModel`. It is as simple as instantiating the ensemble model, calling the method `fit` on the training set, and then, calling the method `predict` with the number of time steps to forecast:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_model = RegressionEnsembleModel(\n",
    "    forecasting_models=[NaiveSeasonal(6), NaiveSeasonal(12), NaiveDrift()],\n",
    "    regression_train_n_points=12)\n",
    "\n",
    "ensemble_model.fit(train)\n",
    "ensemble_pred = ensemble_model.predict(36)\n",
    "\n",
    "series.plot(label='actual')\n",
    "ensemble_pred.plot(label='Ensemble forecast')\n",
    "plt.title('MAPE = {:.2f}%'.format(mape(ensemble_pred, series)))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite easy, isn't it? \n",
    "\n",
    "Now, to get a result similar to the one obtained with the `RegressionModel` approach, just use the `historical_forecasts` method of your ensemble model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "133ffdcbd79a43f297e7c288e3e986a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/34 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5Vygul5bUw5WdI62SnKsewcHBTJw4kYkTJ3LKKafwf//3fx3eh3vuuYeHHnqowfb09HTGjh3bqrYee+wxjj76aBYvXhyo7vnFihUr2Lp1a2d3QxACglKK8upwAPokRpDcS0+QlleFd0p/xEKvR2RkJN9++y3QM2bt//nPf7Jy5Uqfc+PU1NQQEtJ+X4sVK1awcOFCRo8e3W7XEISOoqKiArcRC0BSQgjKpQW90tU5gi4Wuo+kpaVx9913e1LgOrldPv/8c49FP2nSJEpK9JT3X//6V6ZOncr48eO5++67AW1hjxo1issuu4wRI0Zw4YUXsnLlSmbMmMHw4cPZuHGj53rfffcd06dPZ/jw4Tz99NMN+uNyubjllls813jqqacaHHPdddexe/du5s+fz9NPP01+fj5nnXUW48ePZ9q0aZ5FUffccw8XX3wxM2bM4OKLLyYnJ4ezzz6bqVOnMnXqVNauXQvoRVCXX34548aNY/z48bzxxhsA/OIXv8A0TcaMGeMZK8Dtt9/O6NGjGT9+PL/97W9Zt24db7/9NrfccgsTJ05k165dgfhoBKHTKCwshBCdhSshxqBfchQAVe6ozumQUqqzHl2SoKAgNWHCBDVhwgQ1evRo9fLLLyullBo8eLB67LHHlFJKLVmyRF155ZVKKaUWLlyo1qxZo5RSqqSkRFVXV6uPPvpIXX311crtdiuXy6UWLFigPv/8c7Vnzx4VHBysNm/erFwul5o8ebK6/PLLldvtVitWrFBnnnmmUkqpu+++W40fP16VlZWpnJwcNWDAAJWZman27NmjxowZo5RS6qmnnlJ/+tOflFJKVVRUqClTpqjdu3c3GM/gwYNVTk6OysjIUIsXL1b33HOPUkqpTz75RE2YMMFzvcmTJ6uysjKllFIXXHCB+uKLL5RSSu3du1eNGjVKKaXUrbfeqm688UZP2/n5+UoppfLy8pRSStXU1KhZs2ap7777TuXm5qoRI0Yot9utlFKqoKBAKaXUpZdeql577bW2fjxKKaUyMjL8Or8rIWPpmvg6li1btijGfqSY6VIffelW69dvUBxfqZjpUlXV7vbqXpO62mVdLsYJ7VPKSa1u/kdJcy6XRYsWATBlyhTefPNNAGbMmMHNN9/MhRdeyKJFixgwYAD/+9//+N///sekSZMAbdnu3LmTQYMGMWTIEMaNGwfAmDFjmDNnDoZhMG7cONLT0z3XOvPMM4mMjCQyMpKTTjqJjRs3MnHiRM/+//3vf2zevNlTUKOoqIidO3d6Uvw2xpo1azxW9ezZs8nLy/MkFzvjjDOIjNTJhFauXFnHz11cXExpaSkrV67k5Zdf9mzv1asXAK+++ipLly6lpqaGgwcPsnXrVkaPHk1ERARXXnklCxcuZOHChc2+74LQHfG20ONjID4oHmqKIDSRolJISujY/nRZQe+KhIdrv1hwcLCnIOztt9/OggULeP/995kxYwYfffQRSil+97vfce2119Y5Pz093dMGQFBQkOd1UFBQnSKz9X3e9V8rpXj88cc59dRTAzI2J5EY6NS/GzZsICKi5fxqe/bs4aGHHmLTpk306tWLyy67jIqKCkJCQti4cSOffPIJr7/+Ov/4xz/49NNPA9JXQegqFBYWQvBRACTEQFxoraAXdoKgd1kfulod1C6PQLNr1y7GjRvHbbfdxtSpU9m+fTunnnoqzz77rCfxVmZmJocOHWpVu2+99RYVFRXk5eWxatUqpk6dWmf/qaeeyhNPPOFJ0fvjjz9y+HDzqxlmzpzJSy+9BOi870lJScTFxTU47pRTTuHxxx/3vHZ+scydO5clS2qXIxQUFFBcXEx0dDTx8fFkZ2d7EpuVlpZSVFTEaaedxsMPP8x3330HQGxsrGeeQRC6O9pCTwAgPhri4+PBVQjYC446GLHQ61FeXu5xbVRXV3P66ac3G7r4yCOP8NlnnxEUFMSYMWOYP38+4eHhbNu2jenTpwO6tuiyZcsIDvZ9scH48eM56aSTyM3N5c4776R///51XDJXXXUV6enpTJ48GaUUycnJrFixotk277nnHq644grGjx9PVFQUzz//fKPHPfbYY9xwww2MHz+empoaTjjhBJ588kn+8Ic/cMMNNzB27FiCg4O5++67WbRoEZMmTWLUqFEMHDiQGTNmAFBSUsKZZ55JRUUFSin+/ve/A3D++edz9dVX89hjj/H6668zdOhQn98TQehqFBQUek2KQnhYJIarGAXkFtYAoR3aH0N1UiEJJDlXhyJj6ZrIWDoOt9tNUJBvv9J9Hcs9f3yQe1feQrBRQ83nOmQxbOI7VMct4NlbSrj89HapQ9dkDHKXdbkIgiAEiuXLlxMbG8vy5csD2u6hPF1KMiK0Nv95eLDelp3b8emsRNAFQejRVFVVcdNNN1FWVsaqVasC2nZOgZ7Dig6vDWiICNUFLw7lVwX0Wr4ggi4IQo/mmWee8dTxLSoqCmjbecUuAGIiXZ5t0WFayPMKaxo9pz0RQRcEocdSXl7Offfd53kdaEEvLNHrZeKja6cEYyL1toISV6PntCci6IIg9FiefvppDhw4QHjCaBi5jAOFgZ2kLDqs5ycTYmrnKZ0iFwUlHR/3IWGLgiD0WFavXg3A6NkP803OyewtD6xfu6QsCMJ0LnSH+BgD8qD4sG8J8QKJWOiCIPRY8vLyAKgIHglAeXVgbdjDlTrOPMlOmwvQK1YLeWcUuRBBFwShx5Kfr0seZ5X0AaCyOnALfZRSngLRfXrXCnpSQucVuRBBFwShx5KXlwehfSk4rHMmVbnDCdRiysOHD6OCdeqMxPha8Xas9YrqsEbPa09E0AVB6LHk5+dD9ATPa2VEU14emAU/OjFX7bJ/h35JOqldRU1kQK7TGkTQBUHokZSXl1NeXk5w/OTajcHRAQtdzM3NrU2dW5uslH7JWsirVWTAfg34igi6IAg9Esd/HpowpXZjcEzABD07O7tOLnSHpN5x4CpHEUJZRUAu5TMStigIQoejlOJvL8OuA4qIMJgzxWDhcYEN83MiXFTU+NqNwTEUFeUGpP3s7GwI1sVqvF0unhS6wZEUHYboDvS8iKALgtDhbE2HW56odUc8+Zai9CMIDg6cqOfn54MRTmWIVxWv4BiKigJTy1Zb6DOBui6XuLg4XeQiLIWiUuifFJDL+YS4XARB6HDSs/TzmCEQFQEVVYEvCJGXlwfRY4BgBvezNwYFzuWSlZVVmws9tnZ7fHw81BQCUFgakEv5jAi6IAgdToZdwGvaaOiToP8uCHAhK+8Il2mjwcANwZHkFxQHpP2srENghy3GRdVuj4mJAZe+Rl5Rx+ZzEUEXBKHDycjR7pYBydDLtm4Dbc1qC137zycMMwgN1jnLc/IDE7Z4ILsYjCAiw2rquIqCgoIIpQCAvQc7dlZUBF0QhA4n056XHNDH8EwotouFHqELOI8cCOEhOnd5oAT9YI4WaycZlzcRQY6gd2xOdBF0QRA6HMflkppUa6EHWtDz8vIgrC+gJyYjQ3V+8rzCyuZO85nsokhP2/WJCdOD2Z/dsTnRRdAFQehwMnL084Dk2pC/QLtc8vPzISwFgJREiAy309oWV/vdtsvloqi6PwAThjUMFoyL0DO8B/P8vlSraDFs0TTNNGATsMXedA5wInATUA5callWhmmao4Cldpt3Wpb1SXt0WBCE7o9H0PtARWkW0C/gFnpuXj6Eagu9by+I0SvyKSrx32rOzc1FRY4CYOxRDbMqJsZWQSlk5XdsCl1fLfTPLcs60bKsE4EC4Ga0qN8F3Gkfcz9wJTAP+GNguykIQk+h+LCipAwiw6Hy8CFeeelJIPAFIXIKFASFEhdVQ0S4QUyUFteiw/5fJzs7G6JGAzA6reH+wSk6GVhucccu9fFV0GeYpvmFaZr3A8OBbZZlVVmWtRZwlmH1tyxrp2VZxUC+aZodGE4vCEJ3IdPL3fLvfz+Lq1LPkB7K998V4k1usU6V26+XFvC4GC13JWXtL+gjh+hwxqKyqA7N5+LL7eMgMAwoA54GFgHegZzO7w3vm0MR0Buos8bWNM1rgGsAFi9ezNy5c9vW6w6iurqazMzMzu5GQJCxdE2OxLF8uy0MSCQproInnngCao4HYP/BEjIzA7MsXylFYZn2sSTFVZOZmUtYsC3oh90t9rOlsVjf7oGwOQRTTlB1IfUPTUwIh5oSakJi2bbzYJ2ao/6Smpra5L4WBd2yrEqgEsA0zTeBywDv6Qsnct47diceyG+kraVoPztAxxfcayWZmZnNvnndCRlL1+RIHEvlZgUoIox89u/fD70KASirCiM1NTA/7EtLS3EFJQNw1MAoUlNjGNBPT1RWVIe02M+WxpJZoENzEqNyGDgwrcH+iRMnwnMHISQWwlJITe0YX3qLLhfTNL0WtTITeA842jTNMNM0jwM22/sOmqY51D6+t2VZgbnVCoLQo3BCFvfvWq//qNEx24GcFNUhi3q9f0qi3tY7Xvu1y6uC/XaD7MzU7pwBvRpPIzBo0CCoOgB0bKSLLy6X403TvA/tctmDngStAFbZz5fax/0eeA7tgrk70B0VBKFn4KwS3bXlc0JDQzlqWF92AEUBLKpcN2RRtxsfEwwolBFFWVkZ0dHRzbTQPPvzdKzlUf0aj2nv378/VK/Rx2bXAIErfdccvrhcPgA+qLf5FfvhfdxWtAUvCILQJI6bXFVmMGXKFJJSktiRG9iiytpCr41BB4hx0tjaOdH9EfRDpdo11NiEKEBISAgxYaWUAjv2FAOJbb5Wa5CFRYIgdChODDqVGfTv359Eu6hyaUVYwCJCtIWuXS79eutttYIe63fGxaKaAQBMGhXR5DGJsXrZ/679AU4j2Qwi6IIgdCiOD53KDFJSUuidEAWuctwqKGAVfnyx0NtKYYmiJqgPuMqYOKpXk8c51+3I5f8i6IIgdBgVlYrcIggyXFB9iJSUFLsgRGAnRvPy8hsIeqyT4tZPQd+Sbv+KKN9OSr8+TR43OCUM6NjVoiLogiB0GAfsiI/IoAJAkZKSYheECKygH8wpheAoQoOrPUJea6H7Vyh67bc6aju0eifh4eFNHjdskJ44LTjctFsm0IigC4LQYTjulhC3/qPWQi8EApeg64AdSdMrqhzD0BZyjJeFXlhY2Oa2132vXSi9QnY2e9yY4dodU1oV2+xxgUQEXRCEDiOnUD+7Kw8CeFnoekegLPSsAi3iiXG16QQ8FrqfZei+26UncfvHHmj2uKOH9wdXOS6iKA1AugFfEEEXBKHDyLcFu/KwLirqsdBdhUDgLPScQh0CmdK7dlsgJkVLyhTpOVHgrmLq6LBmjx08eBBU6RvXgTwRdEEQOoHq6mpee+01fvaznzF06FA2btwYsLbzbB2tKj1AcHAwycnJ7TIpmlOkrehBKbWiG4iwxXdXHQCCoOx7bv3tr5o9NiEhgSCXdi3t3Nsx1aJF0AVBqMONN97Iueeey4oVK9i9ezdvvfVWwNrOK7Yt1Zo8+vXrR1BQUL1JUf8t2erqaoor9KKhoQNrFw9FhoNh6ELRBYVtu3M8/C+9+nNk/2KGDRvW7LGGYRAdqvMY/vCjTm1VU6OoqWk/a10EXRCEOjgW+fTp0wE7VWyAyHPytFbnk5KiwwoDPSmalZXlKWwxoE/t6lPDMIgM1bkEcwtaX1d09+7dWNu1b/6ys8f4dE7vaH2dHXv04qKPLYiYq7joTw3rkAYCEXRBEOqwZ88eAC655BIgsIKe7wh6Ta2gB3pSNCMjo8EqUYeoCG0dZx3y/c6xNV1x/d/d/Oul1aiYKQAsnNXXp3P79dKrRXcf0DeS9CxwufSvhfagY8tpCILQpSkuLiY/P5+IiAjGj9e1a9rFQq/JIyVFl3CLjo72TIoGwuWSkZEBoUcBkJxQd19clEFuMRzM8f3O8cQKxRMrIDz4/0FEFGHBVRw92DdF7p9YDTmQVaB9+ekH9fjS+rXPYiOx0AVB8JCeng5AWlqax4IOqKA7c5HVeZ72DcMgOkxbsrmFribO9J2MjAwI0aZ5YlzdffGx2obNK6jA7fbN7eH8aqh06UD24SklBAf7JsiOcOeVal9+epaz3afTW40IuiAIHhx3y5AhQ+jbV7sVsrOzA5c0yzGMvVwuALGRWsjzi/33LWsLXa/3711P0GPtuqIuosjN9a1kQ0mZfo6o+g6AuabvuVmGDtBpc4sqEwDYa98bB4ugC4LQ3nhb6FFRUcTExFBZWUlxcXHzJ/qAUqpRCx0gLloLeWGp/66IvfuzIDiG4CB3bf4WG+9Y9IMHD/rUXrEt6NU/3QYb+nHftfE+9+WogXHgKqNaRVNUqsRCFwSh4/C20IE6Vrq/lJRBjQuCVBmoKl0EwqaXvTq+uMx/SdqbqX8GxEXWeJb9O3gn6PJV0B0L3VVZQL/EYKKjo5o/wYvk5CSoTAdg+z7IzofQkNqEYYFGBF0QBA/tKege67xGx2R7W+i9YkNBuSivCqbazzjtjGwdKpjYiCHtvfy/tYKOq4ShQ4e2qi/JyclQsQ+A1dpjw6C++OyDby0i6IIgeHBcLvUFPSsry++2Hf+5uzIHwzA8bQPEx8cGJBbd5XKRk6993H16Nwzi8864eOBA87lYHPwR9KSkJKjcC8Dn3zoRLq1qolWIoAuCAGgft2Ohp6WlAe1koVfnkZycTEhIreAGKoXuoUOHcAUlAJAU31De2uJD9xb0o446qlX9iY6OJsSlbxxrNougC4LQQeTn51NSUkJsbCy9e+uwv4AKeiOLihzi4uKgWidLzy1s+zXqhCw26nKxXR0+CrrbrSh1FpW6SlttoQPEhesblVMEe3A7xaCDCLogCDbe7hZnMjGQgu5ZJVqt87h4owVdFxt1Uuy2BR2yqAW9dyNpyL0TdPki6I6YB6nDgGqToCfF1K0p+sGKJ3j66adb3Y4viKALggDQwN0C7WehJycn19kXHx8P1Toz4aHCtl9DW+g6hCQxvqElHNPKKBfH3aJqtB+otS4XqF3+77D+0//y2WeftbodXxBBFwQBaBjhAngs6cD40O3oleo8PVnohbeFfqig7dfwdrk0ZqHHelnoBw4caHHBlEfQq4uIjo6mT5+ma4g2RWpyELhrC21QkV7nPQ4kIuiCIAANI1wgwC4Xr1Wi9QXd20LPKWx72KL3KtHGfOh9dFU4giJTqaqqoqCg+btHseMtsSNc6se1+0Kf5ESo3A+AQQ1UHWiTpe8LIuiCIACNW+iBXP5fP8rFm7i4OKiyLfTCtl9DW+hatevncYHaCBMjQo+xJbdLiWdCtLjNIpycnOwJXQxX2YBbLHRBENqXjIwMAAYOHOjZFhMTQ1RUFOXl5ZSW+pes3NuH3l4ul/379zeZxwWgf5JeqekKToagiJYF3ROy2LYIF3Bi0fXiIndFOtA2X7wviKALggDoGG6gzoIf79f+ul28U+c273JpW/sFBQX6V4bjcmlE0IODDQY5wwsf3KigP/roo1xwwQWUlJTUiUFvqUJRU+jVotpCryr6keDgYAYMGNCmtlpCBF0QuhEbNmzwOUtga3C73Z5264ttoATdO2yxcQvdjnJpo4W+YcMGAIxmLHTwWtgTkdZA0LOysrjtttv44osv+PDDD+sI+oQJE9rUr6SkJMhbQXDFVsh5hcGDB9dZVBVIRNAFoZuwbds2pk+fzrRp0ygpCVA1ZZuCggJcLhe9evUiLKxuNftALP93uZRe0q/cUFPYhIWubyg5RXpBT2tZu3YtBEWhjHAiwiAqovEJTI+gh6c1WP7/8MMPU1lZCegbRH6xnSrXVeop+NFakpOT4fB3uDaNg8KP281/DiLogtBt+PrrrwHYtWsXv/71rwPatuNuqT9ZCYGx0AtKQCmgphDDUJ6VqA4RERGEBGuxd7nals9l7dq1za4SdRiSYgt9PQu9oKCAf/7zn57XGzZsYM8+fZNJTAjTlZXaQP2bV3v5z6EVgm6a5gWmaebYf59jmuY60zQ/MU1zgL1tlGmaq+3tc9qrw4JwpPLTTz95/n722Wd54403Ata2I+iNxVkHQtBrQxbz6NWrVwOXg2EYdqSL724XpRR33nknK1asoLq6Whe3dtwtjcSgOzTlclmyZAmlpaUcc8wxAHz11Vfs3qsnagelJrTcoSbo3bt3nXDHTrfQTdMMBs4B9pumGQLcDJwI3AXcaR92P3AlMA/4Y8B7KghHOI6gT58+HSCgVnpzgh6IxUXeIYv1LVaHOqtFfRD077//nvvuu48LLriAd955h7KyMlLTxgHNW+hpThqZ8MHs27fPs/2JJ54A4P7772fYsGFUVlbywzY9mXnU4Ia/XHwlODi4zi+SrmChXwC8BriB4cA2y7KqLMtaCziOpf6WZe20LKsYyDdNs/FPTRCENuEI+gMPPEBERAQZGRl+hxI65ORoS7Q5C701PnSlFF/vqPWD10a4FDQp6ElJSbV+9MKWr+H0p6KigiuuuAKAYaOOBXy30DMyMqipqaGoqIgDBw4QGRnJ7NmzmTx5MgC5BRUAjBqW2nKHmsHbldWpFrptnZ8LvGJv6gV416MKbqStIqCuk0wQBL9wBH348OGesDcndtxfmvOhO5kRfRV0t1ux8DaFeY3C2q5F3RcLffDgwa3K5+Id7VNUpC8wYIi2L5uz0FMSISwUCOuLS4WTkZFRp/SeYRgeQSdYh8qMPXpwyx1qBu8xt6eF7kvszEXAq5ZluU3TBCgEvAOCnDLd3tVd44H8+g2ZpnkNcA3A4sWLmTt3bhu63HFUV1eTmZnZ2d0ICDKWromvYykqKiI3N5eIiAjcbjfJycn89NNPfPPNN8TGNmOO+oizSjQsLKxBfxz/b0ZGRrN99R7LwMRYlIrhqgcrefuPefz3415ABFRlEhUV1Wg7iYmJHkH/aW8xmZnN//rYuXMnoCdUKyq0JR0Roy3pUKOUzMymI4FSE5PZkxUCEYPZtGmTp2ZqSkoKmZmZjBunXTcEx+h2Q/37zsXE6Haio6OpqKjwq63U1KZ/Lfgi6KOBSaZpXoR2t/wSONo0zTDABDbbxx00TXMocAjobVlWg2BZy7KWAkvtl4EpI96OZGZmNvvmdSdkLF0TX8fiWMeOdT5s2DDWr19PeXl5QN6LsjIdcD1ixIgG7Tn+35ycHPr3799kPhPvsfz1l4p3vlR8tyuMG5ak8JEF4SGVVB58gsGDf95on8eMGQPv/AhApTuW1NTmizHX1OiQwsWLF/PWW28RFhZGXKJ2Z6SlxpKa2kQgOjBsgJs9WUB4GqWlpZ4w0FGjRpGamkpNTQ3R0dEcDtY3y6GDk0lNbXsec2f17dChQ9ttURH44HKxLOs2y7JOsSxrHrDTsqxfAI8Aq4D77AfA74HngI+Ae9qhr4JwxOK4W5zVio5A7N+/PyDtN+dyiYyMJD4+3qdkVg6xUQYPL9YC+O46vW3WgBVQub9Jl0taWlrt8v/Clq/h+P2HDBnC999/z7fffuupdtTYKtE613ImRiMGk56e3iCPTUhICFOnTgVb0GN9rwvdKM772p7+c/DNQvdgWZZpP79CrU/d2bcVmBm4rgmC4NBRgt5UetiUlBSKioo4ePBggxjypjjnJHj6XVhpwcLjICHzA6BhXLbD4MGDWxW26Ah6cnIy4eHhAOQVac9vU6tEHXQsuoKIIaSnb/HcqLwF9/LLL+fzp+JQ+C/oTrtjx471r6EWkIVFgtANaG9Bby7KBWpDF32twwna9/7SnQYPXmfwwu8N8vIaTy3goCdFfa9a1FiqgnxfLXSv1aJ79uxpNHXwxRdfTFCYztzor6BffPHFvPHGG9x6663+NdQC7ZNQQBCEgOJMALaHoNfU1JCXl0dQUFCT1rcT6dIaQQfo08vg1p/rvx0BbsytAzoOPTayghIgO99NS/amt4Xu4ETTtGShD3YSdEUMZs+ePRQWFgJ1qzVVVIHLpSNiwkL9qwMaHh7OokWL/GrDF8RCF4RuQHMWur95yh2hTUxMJDg4uNFjWhu62Nx1mrLQAYYMiAPlJr/EwOVqflz121NKcdCOrUtOaL4vqc49ICzFE88fFxdHr169PMc4ibni/LTOOxIRdEHo4pSUlJCdnU14eLgnQiIhIYHo6GhKS0s9MdhtpSX/ObTN5VIfXwQ9bfAAqMlDKaN2MVIjuN1u8vLyoO+V7MzSy/13ZUJRKfTr3bKg93V0O6wvoK1v7+LYUCvo/rpbOhIRdEHo4uzatQvQC1KCgvS/rGEYHivd38VFLfnPoe0uF4fKykpKSkoICQnROVuawNuP3tzEaEFBAe7IcTBiKRf9ORilFF9u1fuOHU2LpeIiwg16xQJGqCf/i+M/P5ir2LQjVARdEITAs3v3boAGFXMC5UdvLmTRwV+Xi7d13pzYpqWleSJdspsR9JycHIjWq0L3ZcOOfbBxm3bRHHO0b/7ulET7j9CU2msDF/xR8bN7kvjY0rtF0AVBCBie1ZdepeEAj/slUILeni6XliZEHXTooh7vvnq5wPKKFJfd7+ajjUq3FzXKs+9jCzZu138fc7RvffIIepgW9CFDhpBTqFj9nd787Hv6BiGCLghCwHCKMPTv37/O9kBZ6B3hcvHFfw62oNt1N/dm1Z0Uvf0pxfMfwh/+pXSfo0Z79r23XvGNDgTCHOlbnxoT9A+/tPO2A9vtRIwyKSoIRxgrV67kgQcewO12t3xwK2lvQffFQncqGRUXF3vSBLSGVgm6XVB5r5eFbm1XPPOe/vubnZB5sBAiay30jzZCZRWMHAQJsb65XPo5EZphOoYxLS2N99Y3jKwRC10QjjBuvPFG7rjjDj744IOAt91Rgt6cO8QwDI/bxRc/+n/+8x9mzZrl6buTd7wlQU9KSiJc6fZ3ZehcLW634lePKo/l7HLBpp2REDkUAzdDvdLC+OpuAUhJtIXfttAHDhrCRxv1pmNHVXqOE0EXhCMIpZRnpeFrr70W8Pa7goUOrXO7PPXUU6xevZpHHnkEgNdffx2AmTObzw5iGAapyTqB6+4D+tfO8tWwfgskxdUQlPM8AOt2jQQjmKToQhZOrz3/mFG+LwByXC4xicOZNm0a36dHU1gKIwbC1acd9hwngi4IRxAFBQUeN8SKFSs8RYYDhS+C7uviIqUUf/nLX3T9TRtffOjQOkF3LPJnn32WzZs3s3HjRuLi4jjrrLNaPHdoaigAWQUhuFyKz77RY5vY5wvcudrvsqtwDAADEg8zd2qtiB87Gp9xBH2COY81a9bw/gZ9nQXT4YRxVUTYtbJjo/xbJdqRiKALgp94W8hFRUWsXLkyYG2XlZVRWFhIaGiozhfuRWxsLPHx8VRUVOhFNj6wYcMGbrvtNubPn8+ePXvIyMjwxLG3JOi+ulxcLpenzby8PH7+c732/5xzziEyMrLFPg4Z3A+qsnG5gziYB1vS9fac9JVQtAYAZWctGda/ilkTtBUdHwPjhzbRaGPjsX3o2QUGwcHBvL9Bv14w3SAqQjHX1K9bygvTlZBcLoLgJ/VdHq+++ioLFiwISNuONdxUHvKBAwdSVFRERkZGi/5p776WlJRw0UUX4XK5qKioYN68eSQkJDR7rq8WelZWlidXOcCWLVsAnaDKFwYNGgQb90JYX/Zmwxad2ZbtX78B1dlQvhMihwMwZohBTJTBF/8Aw4DwsNa7XA7mQ/FhxeZdOm/L8eMgNwce+ZXBmCGK82b73GSnIxa6IPiJY40ef/zxALz11lsBc7t4C3pjtNaP7m1dr1u3ji+//JJBgwbxwgsvtHiur4LuuFtGjhzpqdQzePDgFv3nDgMHDoQKXZx50zadeTEqvIbKop26vaJad9HEkREATBhmMH5o61wjcdEQGQ6Hy2GNXaZn7JDam8JR/Q0euDaI6EhxuQjCEYMjpnPmzGH8+PEUFRXx2WefBaTtpvznDm0V9Dlz5mAYBuHh4bzxxhstLvgB3xcXOX0ZPXo0l1xyCQCXXnqpJ21BSwwaNAgqtaB/8KX2aydG6BjGiy66iF7BWz3HHjuu+apGzWEYhsdKd/znk0e0ubkugQi6IPiJI2ADBw5k9mz9+/zbb78NSNstCXprV4s6gn7eeeexdu1aNm3ahF0ruEUGDRoE4InoaQrHQh80aBB/+ctfePHFF7njjjt8uobnOraFvupbvc1d+gMAJ554IgtnxoByQfk2+ibF+NxuYzh+dMd/Pml497HGG0N86ILgJ47LZeDAgZ6FRVu3bm3uFJ9pLwu9X79+TJ8+vYWj6+JUq9+zZw9ut7uOxb1u3Tq++uorfvnLX9YR9OjoaC666KJWXSc1NdWzuKiqWm/L2bsKgFmzZpGcnMyLi06hXyIYhn+/hBwLfY/9o2PScL+a63RE0AXBT7wt9KgoHbS8bdu2gLTdnoLeWmJjY+nTpw+HDh3iwIEDDBgwgMrKSn7/+9/zt7/9DYDjjjuujqC3hfDwcBJjDuMdt1NV8C0jR46kX79+9OnThztu+CQg5dxSvAKHDKN1UTJdERF0QfADpZTHQh8wYIAn9G/btm0NrNi20JUEHbSVfujQIXbt2kVqaioLFizgk08+8exfuXKl34IOMLgfdQSdsi3MmqUjh4KCgvjzn//c5ra90atFtf981CC61QRoY4gPXRD8IDc3l4qKCl0+LTaWxMRE+vbty+HDhwNSHs5XH3pGRkaLeWTcbrfPq0Kbwknhu2vXLrKzs/nkk0+Ijo7mlltuAeDTTz8NiKAPGdgbqnX+3BCjDKoyW+0i8gVPPhe6v7sFRNAFwS+8/ecORx+tE4oEwu3SkqBHRUWRmJhIdXW1R6ybIi8vD5fLRe/evQkPD29TfxxB3717N5s361i/yZMn85vf/AaA1atXk5eXR1hYWJtvGlA30iW4YgeAz5O3rcHb5dLdJ0RBBF0Q/MLbf+4werRef+7vxGhJSQklJSVERkYSH990eJ6vbhfH3dK3b99mj2sOZ2J0165dfP/99wCMGzeOvn37MnLkSCoqKjx98sfdpAVdW/qVBV8TFRXFqFGjWjir9XgLencPWQQRdEHwC0dEHdcHBM5Cb2mVqIOvpej89Z9DXZeLt6CDnhCt36e2MmjQIDis26dkE5MmTSIkJPBTft6CPrEHuFxkUlQQ/KA9LfSW3C0OrbXQAyHou3fv9izvHz9el4KbMWMG//73vwH//OdgjynjUij9FvJWYJ73S7/aa4o+veC82ToXTO+47u9yEUEXBD9ozIfuLehKqRYLFjdFVxT0fv36ERkZSV5eHkVFRQCe8MHp06cTFBSE2+32W9AHDRoErlLIexNoH/856NWiL9/T/YXcQVwuguAHjblc+vbtS0JCAoWFhWRnZzd1agPeffddrrzyStLT01FK8fHHHwO1OVSawtfVooEQdMMwPH70mpoaBg8eTFycTkcYHx/P5MmTAf8t9OTk5DoTt+0l6D0NEXRB8IPGXC6GYbTJ7XLvvffy7LPPMn78eE477TSee+45QkJCWLRoUbPnNWehf/PNNwwfPpx//etfARF0qHW7QK27xeF3v/sdM2bM4PTTT/frGkFBQZ4bVWxsLCNG9IAZyw5ABF0Q2oh33u/6k4COoLdmYnTXrl2Ajm758MMPiYqK4p133mkxS2FTgl5dXc3ll1/OTz/9xF133eXZ76+gOxY61E6IOixatIg1a9b4fQ2otfKnTJni9wKtIwV5lwShjRw4cIDq6mr69u3rWfLv4ITYbd++3ae2CgsLKSgoICoqimeeeYb58+fz6aefMm/evBbPTU1N9fTH5XJ5tj/88MN89913gI6YWbNGF4cIpIVeX9ADiSPo4m7xnRYnRU3T7AssB6oBF3AhMBT4C+AGfmFZ1vemafYDXgCigScsy1rWbr0WhC6Ak3UwLS2twb7hw3UM3M6dO31qa88eXcXhqKOO4oorruCKK67wuR/h4eH07duX7OxsDh48yIABA9i9ezf33HMPAGeccQZvv/22p0ydP3HoTh8d6rtcAsmFF17Ipk2buPTSS9vtGj0NXyz0XOB4y7JmoQX7SuDPwALg58CD9nG3oUV+FnCDaZoRge+uIHQdAinou3fvBuqKZWuon9r26aefpry8nPPPP59///vfRETof8egoCCfKhs1h2Ohh4WFecbZHsydO5ctW7YEJAnXkUKLgm5ZlsuyLCdJRCywC3BZllVgWdY+wMmGcAzwqWVZNYAFyKcgdDo//fQTqampPPzwwwFv27GqhwwZ0mDfUUcdRVBQEOnp6VRVVbXYlr+CPnLkSKDWxeOUfTv77LPp3bs3559/PqBzuAQHB7fpGg7Dhw/nqquu4u677yY0NNSvtoTA4pMP3TTNiaZpfgksBtYBxV67a0zTDANCvYS/iFqhF4RO4/333+fAgQPceuut/PDDDwFtuzkLPTw8nEGDBuF2uz3C3xz+Cnr9qBpH2B1f/vXXX49hGJ5VrP4QFBTE008/3aqiFULH4NPCIsuyvgWONU3zXOD3gHcd7BDLsqpM06w2TTPIFvV4IL9+O6ZpXgNcA7B48WLmzp3rb//blerqajIzMzu7GwHhSB3LN998A+iY6UsvvZQVK1YELGJixw6dNComJqbR/gwaNIj09HQ2bNjgqa1ZH2csTjRMfHx8mz4nJxHWN998w+7du9m9ezdBQUFERUWRmZlJ//79efvtt+nXr1+7fQ+O1O9YR+NMgjeGL5OiYZZlOb8Zi4BSIMQ0zQS0C8YR7k3AiaZprgamALfWb8uyrKXAUvul8rH/nUZmZmazb1534kgdi5MPxTAMvv76a9555x2uv/76gPTDads0zUb7M3bsWFavXk1+fn6T/XXG4ojH1KlT2/Q5OaGNu3fvpqKiApfLxbBhw+pY/O39+R+p37GuhC+mykTTNFebpvkZ8Gvgr8AfgPeBl4Hf2cc9aP+9GnjSsqzywHdXEFqHMyl57733AvD4448HpN2amhpP3u/Bgwc3eoyvE6Mul6tZ940vHHXUUYSFhbFv3z42bdoE0C7ZCYWuTYsWumVZG4ET6m0+CBxX77iDQNf2oQhHFFVVVaSnpxMUFMSNN97IPffcw48//khFRYUn6qOtZGZm4nK5SElJabItXwU9IyODmpoa+vfvT2RkZJv6ExISwogRI/jhhx9YsWIFIIJ+JCILi4Qei1PMeNCgQcTFxTFs2DDcbjc//vij3237YlE7y9WbEnSXy0VlZaXfE6IOzsTohx9+CIigH4mIoAs9FkdIHUt5zJgxQG1Inz80F7LokJaWRnBwMPv27fMUfnDIyMhg9OjRHH/88Xz66aeA/4LuRLA41xJBP/IQQRd6LPUF3bFgAyHovljooaGhDBkyBKWUxwoHOHToECeffDI//vgjBw8e5L777gMCZ6E7iKAfeYigCz2W9rTQfZ3ErO9Hd7lcnHbaaezYsYNx48bVOT9QFjpAUlISiYmJzRwt9ERE0IUei+Mr7yyXi/e1nb5s3bqVr776ij59+vDxxx/z7LPPEhsbC9Su9mwrI0aM8MTYB2IBkdD9EEEXOp1t27bx6KOP1skUGAjqW+gjR44kODiYXbt2NfBpt5a2WuiO62XKlCn07duXESNGsGrVKp588kmmTp3qV5/Cw8M9eVbE3XJkIiXohE4lLy+POXPmcPDgQY466ii/CyM4VFRUsH//foKDgz1WdHh4OMOGDWPHjh3s2LGDCRMmtKrN0tJS3nvvPTZu3EhGRgaGYbRYDNmJdHFWlTYW0TJ58mRPpR9/GT16NDt37vTb2he6JyLoQqehlOLqq6/2rLjcvHlzwAR9165dKKUYMmRInQRSo0ePZseOHWzZssVnQS8qKmLx4sW88cYblJfXrpebOHFinTJpjeFYyo6gO0Us/PWXN8VNN91EdXU1F110Ubu0L3RtRNCFTuOZZ55h+fLlntetKdfWEvXdLQ5jxoxh+fLlrfKjv/nmmyxbptP7z5gxg3nz5jFhwgRmzZrV4rkDBgwgMjKS7OxsCgsLPRa6d5GIQDJr1iyf+iX0TMSHLnQKSinuuusuAE9uldaUa2sJxyJuTNChdROjTr/uuusu1qxZwx/+8AdOP/10T3Hk5ggKCqrjdgnUIiJBaAwRdKFT2LJlCwcPHiQlJcUTh719+3bcbncLZ/rGt99+C9DAreIIemt+DTgRKvXjvH3Fcbts27bN5+gYQWgLIuhCp/DJJ58AMHv2bHr16kVKSgrl5eXs3bs3IO07aXMnTpxYZ/uIESM8kS5lZWU+teVY+22daHTO+/TTT6mqqqJv375NptMVBH8QQRc6BUfQ58yZA9TGTQfCj3748GF+/PFHQkJCPBa5Q3h4OKNGjcLtdvvkdqmpqfFMZLa13Joj6E6OFXG3CO2FCLrQ4dTU1PD5558DtYLuuDMC4Uf//vvvUUoxevToRqNQHDfMd99912Jb6enpVFdXM2DAAKKjo9vUH0fQc3JyABF0of0QQRc6HMuyKC4uZtiwYZ7ixoG00B13y6RJkxrd3xpBd/zn/sR11z+3vSJcBEEEXehw6rtbILAWujMhWt9/7tAaQffXfw66RJ139Rux0IX2QgRdaJaampqAt9mYoHtb6Er5V52wqQlRB0fQN2/e3OS1KisrgVpBd0IP24r3DUEEXWgvRNCFJtm5cyd9+vRh8eLFAWszNzeXtWvXYhgGJ510kmd7nz596N27N8XFxRw4cKDN7dfU1PD9998DTQt6v3796NOnD0VFRY1G1bz22mtERkayZMmSgLhcoG5uFXG5CO2FCLrQJA888AAFBQW89dZbAWvzueeeo6qqivnz55OUlOTZbhiGx0r3x+2yY8cOKioqSEtLIyEhocnjxo8fDzR0uyiluOeee1BKccstt3isfX8F3Tk/IiKCfv36+dWWIDSFCLrQKBkZGZ7l7hkZGRQWFrbq/P3793PKKafw//7f/+OPf/yjpxzc0qVLAbjuuusanNOWAhTbt29n1apVnteO/7ypCVGHpvzoK1eu9EzMlpeXU1hYSHh4uGfytq04gj5kyBBPiltBCDTyzRIa5ZFHHqG6utrzurU5xF955RU+/vhj3njjDe6++26OPfZYnnrqKXbu3MmAAQOYP39+g3McF8lXX33l83XOPvtsTjrpJN5++20ATzm3ptwtDk0J+iOPPALAr371K4+FP2zYMIKDg33uU2PMmjWLs88+m9tvv92vdgShWZRSnfXo8mRkZHR2FwJGa8aSn5+vYmJiFKAmTpyoAPXkk0+26nrXX3+9AtQFF1ygZs2apQDP49577230nE2bNilAjRw50qex1NTUqJCQEAWopKQktWTJEgWo4OBg9fXXXzfbxnfffacANXToUM+2HTt2KEBFRESonJwc9cwzzyhAXX755a0ae2s4Ur9jXZ0uPpYmdVUsdKEB//znPyktLeXkk0/mwgsvBOCHH35oVRtOzpLzzjuP9957j2OPPRaA4OBgrrzyykbPGT9+POHh4ezYscMnF8+hQ4c8UTi5ubnccMMNAPz1r39t0eUyatQoQkND2bVrFyUlJQA8+eSTAFx88cUkJSVxxRVXsGnTJh5++OGWBywIXQARdKEO5eXlPProowDcdtttjB07Fmi7oA8ZMoTo6Gjee+89FixYwF133VUnJtubsLAwjxBbltXiNTIzMwEYPHiwp37meeedx69//esWzw0LC/NMjDrXcnzxzk0MwDRN4uPjW2xPELoCIuhCHf7973+Tk5PDlClTmDNnjkfQneX0vqCUalCiLTExkXfffdeTMrcpjjnmGAA2btzY4nUyMjIAGDduHB9//DF//vOfeeaZZzAMw6d+Tps2DYAvv/yS8vJyNm/eTFBQEKZp+nS+IHQ1RNAFDzU1NTz00EOAts4NwyA1NZX4+Hjy8vI4dOiQT+1kZWVRUVFB7969fcoZ7k1bBH3AgAFMmjSJO+64o1X5VhxB37BhA9988w0ul4uxY8e2OWeLIHQ2IuiCh+XLl7Nnzx6GDRvGokWLAB0f7m2l+4I/Ob8dQf/yyy9b/EXgLehtwVvQv/zyyzrXF4TuiAi64OGzzz4D4KqrrqoTptdaP7rjbmmLoA8bNoyEhASysrI8gt0U/gr60KFDSUxMJDs7m9deew0QQRe6NyLoggdnhWb9Kj+tFXR/LHTDMHx2u/gr6IZheKz09evXAyLoQvdGBF3w4KyQdJbgO3SkoAOeEMf2FnSodbsAREVFNSiIIQjdCRF0AcAz6RkdHc3AgQPr7HMEffPmzVRUVLTYlr+C7oQuNpfeVinlEfSmwiB9wVvQp0yZQkhISJvbEoTOpsVvr2maxwCPAtVAJnAJcBZwE1AOXGpZVoZpmqOApXabd1qW9Ul7dVoIPI675eijj26QayQpKYlJkybxzTff8Omnn3Laaac125a/gt7UsvzDhw/z3HPPERISwtlnn01lZSUJCQl+1eecOnUqhmGglBJ3i9Dt8cVC3w/MtizrBCAdOBO4GTgRuAu40z7ufuBKYB7wx0B3VGhfmnK3OJx++ukAvPPOO822U1NTw759+wC94KctpKWlERsbS1ZWlidU8vnnn2fYsGEsXryYX/ziF56oFH/cLQDx8fGepGBTp071qy1B6GxaFHTLsg5allVuv6wCRgLbLMuqsixrLTDe3tffsqydlmUVA/mmaSY11p7gH48++ihnnnkmZ555JjfffHPAClA4FrojbvXxFvTmwgkzMjJwuVz079+fiIiINvUlKCioTnrbzZs3c9lll5GVlUV4eDhKKU+8vL+CDnD//fdz2WWXccYZZ/jdliB0Jj47DE3THAycAtwOJHvtcuLbvG8ORUBvILdeG9cA1wAsXryYuXPntqHLHUd1dbVneXlXIDc3t8Gy9tGjRzeaubA+LY3Fyfvdp0+fRo/r27cvffv2JTMzk48++ohx48Z59tXU1LBu3TrWrl3rOTc1NdWv927YsGGsXbuW1atXe24gZ555JvPnz+e6667zLNPv1auX35/RlClTmDJlCvn5+X610xa62nfMH2QsHUNzc0Y+CbppmnHAi8BlaAH3Xv7nsp/dXtvigQb/HZZlLUX72UFn3uvSZGZm+jXhFmi++OILQIfWTZ06lSVLlvD2229z1VVXtXiu91i2bdtGaWlpHRfD7t27AZg5c2aTYz7zzDNZunQpX375JfPmzQPgzTff5Prrryc7O7vOsRMmTPDrvTvuuON4/vnnSU9PJycnB9B5Wn72s59x6623UlxcDOjScF3pM2otXe075g8yls6nRZeLaZohwMvAvZZl7QB2AkebphlmmuZxwGb70IOmaQ41TTMW6G1ZVm4TTQptxKnFuWjRIu666y5CQkJ4//33ycrK8rmNsrIyZs6cyXHHHcfOnTsBKCkpYf/+/YSFhTU7kem4JLz96H/+85/Jzs5m2LBh3H777fz1r3/l0Ucf5b777mvLED04LpdNmzaxevVqAE466SQiIiLq/CIJhMtFEHoKvljoFwDHAneapnkn8ATwCLAKqAAutY/7PfAc2oK/O8D9FKhbXLlPnz6cdtppvP322yxbtozf/va3PrXx8ssvk5eXB+g0s0uXLmX79u2ArqrTXNje7NmziYyM5KuvviIzM5Po6Gi++eYbwsLC+O6774iKivJzhLWMGzcOwzA8RZrHjBnjKd22aNEiXnnlFUAEXRDq0Fyy9HZ+dHm6UpL73bt3K0AlJCSompoapZRSy5cvV4AaPXq0crvdzZ7vjMU0TU+hibCwMJWZmamee+45Bahzzz23xX6cccYZnoIXb7/9tgLUzJkz/R9gIwwfPtzT11/96lee7Xv37lUDBw5UgNq5c2e7XLuj6ErfMX+RsXQYUuCiu+NY5yeddJInz8qCBQtITk5m69atPuUPtywLy7Lo1asXp59+OlVVVdx///2etpuKcPHG2+3iTEzOmjWrLUNqEe8UBHPmzPH8HRwczHvvvcfy5csZNmxYu1xbELojIujdBG93i0NoaCjnnHMOAB9++GGLbTgVeS677DLuvfdeAJYsWcKLL74ItFyHE/RNBHQx5Q8++ACAE0880bdBtBJH0IOCghrcNMaNG8dZZ53VLtcVhO6KCHoXYu/evSxevJiDBw/W2V5TU+Mpfuwt6KD92lBbbacpdu/ezbJlywC49tprmTRpEmeffTagc6c8+eSTLFy4sMU+9uvXj2OOOYbKykq2bdtGaGgo06dP92l8rcUpNDFt2jSpGiQIPiCJK7oQDz30EEuWLKG8vJxnnnkGALfbzeWXX86hQ4cYMmQII0eOrHPOCSecAMC6deuorKwkPDy8QbtKKX73u99RWVnJJZdc4mnjP//5D0VFRSQnJzc4pznOOOMMT+KsY489NqCTod6ceuqpPPHEE+3m0hGEnoZY6F0IZ3HPq6++yuHDh1FKccMNN7Bs2TKio6P5z3/+06C8WnJyMmPHjqWioqLJ7IQvvPACa9euJTExkb/97W+e7WFhYa0Wc6hdNQrt524Bnd72uuuuazIdgSAIdRELPUDs27ePBx54gPLyckJCQrjhhhtarDzvjdvtZvNmHdJfWlrKG2+8AWi/d3h4OO+8806dzIDenHjiifzwww+sWrWKmTNn1tn37bffcvPNNwPw97//naQk/zMyjBs3jrS0NNLT0znppJP8bk8QhADRXAhMOz+6PK0JXfrFL37hCbED1IQJE1oMJfTGCUt0HlOnTlXJyckKUM8++2yz577++usKULNnz66z/aOPPlIxMTEKUCeffHKr+tMSa9euVQ8//HBA2/SVLh5S1ipkLF2TLj6WJnVVBL0ZWvOhOjHT9913n+rXr58C1Pvvv+/z+U5M+bRp01RkZKRH2GfOnNmiaB46dEgBKiIiQlVUVCillPrhhx9USEiIAtTPf/5ztWvXLp/70tXp4v9srULG0jXp4mOROPT2ZN++fezcuZO4uDhuu+02j4vj/vvv97kNJ/f3zJkzPdEnISEhPPHEEw385vVpzI/+2muvUVNTw7nnnsuLL77Y6GSpIAg9CxH0AODEiM+aNYuQkBCuu+46EhISWLNmDWvWrPGpDUfQJ0yYwE033URsbCx//OMffS6J5viy33vvPQD+97//AXDRRRc1KFghCELPRP7TA0D9RT+xsbH88pe/BHS+FF9wBH38+PFMnjyZoqIifve73/ncB8eqf/nll8nPz+fLL78kJCSkXaNQBEHoWoig+4lSqtFVnNdddx0An376KS6Xq9FzHYqLi9m9ezdhYWGMGjUKoEU3S31mzpzJwIED2bt3L3/6059wu93MmDGD2NjYVrUjCEL3RQTdT7Zt20ZWVhZ9+/at4x7p378/aWlplJaWsmXLlmbb+P777wGdSyU0NLRN/QgKCuKCCy4A4LHHHgPglFNOaVNbgiB0T44IQT948CBr165l7dq17N+/P6BtO9b57NmzG1jVzpL4DRs2NNvGW2+9BdRNRtUWLrzwQkDHtINeaSkIwpFDjxf0goICxowZw/HHH8/xxx/PqFGj2Lt3b8Dad3Ko1M+xAngWAjUl6G63m5tuusnjZ1+0aJFffRk/fjxjx44FIDExsVULmwRB6P70eEF//vnnKSgooF+/fgwaNIiysrI6y9/9QSnlKQvn5FTxxhH09evXN9hXUVHBeeedxyOPPEJoaCjLli0LSJHiiy66CIB58+ZJdIsgHGk0F6Tezo92x+VyeRb8vPnmm2rz5s2eBTjZ2dktnu+9uOC///2vWrFiRZ3927dvV4Dq27dvo4t/KisrVXh4uAJUfn6+Z3tubq6aMWOGAlRcXJz65JNP/Bhlw2s+/vjj6sCBA02OpbsjY+mayFg6jCNzYdGnn37Kzp07SU1N5fTTT2fcuHEsXLiQiooKHn30UZ/b2bVrFxdccAFnnXUWzz77rGe7E2N+/PHHNxqVEhYWxpQpUwDqJM665JJLWLt2LQMGDGDt2rWeFLiBICwsjMWLF5OSkhKwNgVB6B70aEH/5z//Cej8306tzDvuuAPQhR2Kiop8asepXwlw1VVXeV477pb6CbG8qe92qaqq8kykrlmzxuPzFgRB8JceIegVFRUNtmVkZPDWW28REhLCVVdd5dk+ffp0Zs6cSVFREa+//rpP7TsCPn/+fJRSXHzxxezYsaNVgu5MjG7evJnKykpGjhzJ4MGDfRugIAiCD3R7Qf/HP/5BZGQkZ511Vp1476VLl+J2u1m0aFED98PFF18M4ElR2xxbt25l8+bNJCQksGLFCi699FKqq6u5+uqr2b17NzExMYwfP77J853QxXXr1lFdXe1xvRxzzDGtHqsgCEJzdGtBd7vdnoiVt956i3HjxvHUU09RVVXF008/DcD111/f4LwzzzyToKAgVq5cSWFhYbPXcKzzRYsWERYWxv33309UVJTHOj/uuOM87pzGGDBgAKNHj6akpIQvvvhCBF0QhHajWwv6mjVrSE9PZ8CAAVx//fUopbjxxhu5//77ycrKYvTo0Y2GE/bp04eZM2dSXV3tSWbVGEopXn75ZQDOP/98QK8A/c1vfuM5pjl3i4NT4efdd98VQRcEod3o1oL+wgsvADpqZMmSJVx55ZVUVlZ6Ktpff/31TeZEcZJZNed2+eijj/jxxx9JSUmpU5nnlltuoU+fPgA+1bt0ii+/9tprbN++nbCwML9XhQqCIDSguZjGdn74RVlZmYqNjVWA2rZtm1JKqaKiIpWWlqYAFR0drYqKipo8PyMjQwEqMjJSlZaWNtjvcrnUyJEjFaAef/zxBvu//vpr9fTTT/tUsae6ulr17t3bU7TimGOOacVIA0MXj6ttFTKWromMpcPoeXHor7zyCiUlJRxzzDGeDIVxcXG88MILxMTEcNNNNxEXF9fk+ampqUybNo3y8nKP2+XQoUOcd955PPjggzz33HPs2LGDQYMGcfXVVzc4f9KkSVx11VU+ZUUMCQnhtNNO87wWd4sgCO1BtywSvWbNGm644QYArrzyyjr7Zs6cSWFhIcHBwS22c/7557Nhwwaef/55zj33XP72t7/x6quv8uqrr3qOueuuuwJS7WfhwoUsW7YMEEEXBKF96HYW+tdff82CBQsoKyvjsssuqxNj7uCLmAP8/Oc/JyQkhA8//JC9e/fy3HPPAXgs/qOOOopLLrkkIP0+9dRTPdEwIuiCILQH3cpCd2pkFhcXc8455/Cvf/3LrwRUycnJnH766SxfvpwLL7yQQ4cOMXr0aL7//nvWrVtHVFRUm/OT1ychIYF//OMfZGVlMWLEiIC0KQiC4E23EvSQkBBeffVVHn74YZ555hmfLfHmuPzyy1m+fDlr164F4OqrryYoKIjjjz+ezMxMv9v35tprrw1oe4IgCN60KOimacYDHwOjgWmWZf1gmuY5wE1AOXCpZVkZpmmOApbabd5pWdYn7dHhyZMn8+KLLwasvfnz59O3b1+ys7MJCwvzrCIVBEHobvjirygDFgCvA5imGQLcDJwI3AXcaR93P3AlMA/4Y6A72l6EhIR4/OSLFi0iMTGxk3skCILQNlq00C3LqgZyTNN0Ng0HtlmWVQWsNU3zIXt7f8uydgKYpplvmmaSZVm57dHpQHPnnXfSu3dvrrjiis7uiiAIQptpiw+9F1Ds9dpxZHtb+0VAb6COoJumeQ1wDcDixYuZO3duGy7fPlx88cVUV1fX8ZvXf92dkbF0TWQsXZOuPJbU1NQm97VF0AsB7xU7LvvZ7bUtHsivf6JlWUvRfnbQqya7NJmZmc2+ed0JGUvXRMbSNemuY2mLoO8EjjZNMwwwgc329oOmaQ4FDgG9u4u7RRAEoafgk6Cbpvk+MBEYCTwFPAKsAiqAS+3Dfg88h3bB3B3QXgqCIAgt4pOgW5Z1WiObX6l3zFag5VyygiAIQrvQ7Zb+C4IgCI0jgi4IgtBDEEEXBEHoIRhKdfnoQUEQBMEHxEIXBEHoIYigC4Ig9BBE0AVBEHoIIuiCIAg9BBF0QRCEHoIIuiAIQg9BBF0QBKGHIIIOmKYZbT8bnd0XfzFNM8p+7gljGWw/94SxHNsTxgFgmuagzu5DoDBNs1dn9yGQHNELi0zTPAW4GjgAPGhZ1oFO7lKbMU3zLOAiYD/w124+lijgL8BA4P/ZVbO6JaZpTgAeBTYAd9mVvrolpmnOAxYDlcB/gQ8tyyrt3F61DdM0ZwG/QRfhWQJssSyronN75T9HuoX+c+BfwA/AdaZpdstskaZpLgQuBx5EFyC5zd7eLS1Cy7LKgCogFj2ubjsWdAbS+y3Luh04qrM701ZM0wwGrkMXqLkXXQshuht/LucB/0bfmE4Dzu7c7gSGthS46LbYlt95wBogG9gHbAQ+s7dPMU1zV3ewbu2xXAB8AHwNXGVZVo5pmj8CL5um2ceyrEOd2kkf8fpcVluWtcsWiZ+AN4Ffmab5oWVZ+zq1kz7i/R2za+yWAfNM07wdXQRmE/COZVm7OrOfvmCP5Xzgc6AU+B79a3Yvuj5CJBCKvvl2aUzTjEQXtf/QsqzPgT3AQfT/fwWwwDTNUZZlbe/EbvrNEWOhm6Z5AbooRxSw27KsYqAfMN3+GfwNEIEun9el8RpLBHDIsqwDtpgHoa3aPd1IzJ2xRKJvsFiWpYDR6M/iTeBa0zQHdlYffaXeWNLtzVFACvBb4Hq0u2JBJ3SvVdQfi2VZ2cAnaLfeN2hXxdXADZ3VR1+xvzv/RRtx6+3NBjAEXQpzK/q7N6xTOhhAjghBN00zDjgX+BP6S3myaZpJwBPAVaZpRluW9QMwGEjrtI76QCNjOdE0zVEAlmW50QJSYx87qCv/JK43lk+BWaZpjrF3f47+5XEYLSK/ss/pkt/ZRsZykmma/YE30FbsQMuyitBC73w+XfKzaeQ7Nsc0zeGWZa0CVgJLLMu6CHgXCDNNM6irjsUmBHgb/Uv8l6ZpHgd8BBwHjLEsKw9tHEVC1/1cfKHHToraM/G/Bd4D1gInADcBYcA7wCXALOAa9Af+Bdpf+4ZlWe92Rp+booWxvI0ey5mWZaWbpnkl+otaBCQCN3SliSsfx3IKcC1wIrpG7QHgsGVZd3ZCl5vEx+/YHPQ4xqMtwdOAnyzLurcTutwkPn4u89G/LlLQgrgYKLAs61ed0eem8BrL2+j5sQH260y0kXAZ8H/AOHTB++3AQrTL71+d0OWA0SWtHX8xTXMA8De0r68f8IJlWe8DfwVOsizrIeAF4C+WZT2I/gJfC2zugmLe0lj+hp7cedA+ZRBa0HdalnVpFxNzX8byAnAP8BDwrGVZ51uWdXMXFHNfvmPPo6OnXkP/5D8WWNcFxbw1n8uz6ELx9wAbu6CYe48lFfinZVkW2ripsizrJXv/KcCLaJfeLGBTdxdz6GGCbprmCV4/lxIsy/qbZVnPA7Gmaf7Osqz/oX1noAtdR5mmGWv/lLzUsqyHO77XjdPKsfwD+2c8+ifxdMuynujgLjdJK8fyKNpqwrKsZfb5XeZ72oaxhJmmGWfX3P1NN/9cooEIy7L+i/5F+HgndLtRmhlLvGmaVwF/Bo4BsCzrQ2CUfdwPwK+60lj8ocv8o/iDaZoxpml+jPb3nYaesFljmua19iFfAGeYpplgWZbLNM0TgBXoSIpSAMuyahq23PH4MZbdAJZlfWFZVmHH97wh/nwudugi4Jkb6FT8GMsuewIey7JcndD1Bvj5uRwG6Crx9D6MZTVwhf28xjTNu+3jD9jHdpnPJRD0GB+6aZpT0AtRjkEvFEiwn9PRon0Ybb1uAZ5G/5x/ozP62hIyFhlLe3OEjaUSfUNaD/RFT4T+rxO62u70GEF3ME3zMbRvb5lpminon+8/Ab8GXrIsK6sz+9caZCxdExlL16SFsbzYXUJ5/aFHuFygTqjRS+iQsT6WZR1ExzK/hg5JLOlK/timkLF0TWQsXRMfx1LancMRfaXHWegApmn+EhgKFAC7gB8ty9rYub1qGzKWromMpWvSk8bSFrr83bc1eFkT49Exs7sty1rWHT9QGUvXRMbSNelJY/GHnmqhnw28a1lWZWf3xV9kLF0TGUvXpCeNpS30SEEXBEE4EulRLhdBEIQjGRF0QRCEHoIIuiAIQg9BBF0QBKGHIIIuCILQQziiStAJRyamLqV2K7ryznOmaV6GTjl8i53mVhB6BGKhC0cCUcDd6MIGoKshXYAuQiEIPQax0IUjAct+nmWapkIXPxgM3ALsME0zHUhCF6S4CF1E/B/oCvchwOWWZX1ommYYcD/6ZhANfAxcb1lWTgeORRCaRCx04UjgDvt5G1qMG3OzRNvP69F5tZ9AV+zpgy5XBvA74Ddoy/4R9BLzJ9ulx4LQBkTQhSMBJ/f1IcuyXsYualIPN7qGppPz+0XLsh5DF0IYYm9baD9fi3bhRANz26XHgtAGxOUiHAn4kt+i3LKsKtM0q+3XRfazCwj2Oq4GLexOlRsxioQug3wZhSOBYrQFPsw0zQvR/vO28C7aCLoUXYx7HtpaF4QugQi60OOxLKsa7Q9PAJZRa123lgfsdmaiJ03noyNmBKFLINkWBUEQeghioQuCIPQQRNAFQRB6CCLogiAIPQQRdEEQhB6CCLogCEIPQQRdEAShhyCCLgiC0EMQQRcEQegh/H+aCe1KkfkWvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_pred_hist = ensemble_model.historical_forecasts(series,\n",
    "                                                    start=pd.Timestamp('19580101'),\n",
    "                                                    forecast_horizon=3,\n",
    "                                                    verbose=True)\n",
    "series.plot(label='actual')\n",
    "ensemble_pred_hist.plot(label='Ensemble forecast')\n",
    "plt.title('Historical forecast: MAPE = {:.2f}%'.format(mape(ensemble_pred_hist, series)))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the when we use `RegressionEnsembleModel`, by default, we are using the `LinearRegression` model from scikit-learn. To change this, you can pass to the `regression_model` argument any regression model with `predict()` and `fit()` methods, for example `RidgeCV()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FFT and RNNs\n",
    "If you'd like to try models based on Fast Fourier Transform or Recurrent Neural Networks, we recommend that you go over the `03-FFT-examples.ipynb` and `04-RNN-examples.ipynb` notebooks, respectively. Notebooks after that give more examples (essentially of neural nets based models), and notebooks 09 (DeepAR) and 10 (DeepTCN) showcase probabilistic versions of RNN and TCN models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A final word of caution\n",
    "So is Theta, exponential smoothing, or a linear regression of naive models the best approach for predicting the future number of airline passengers? Well, at this point it's still hard to say exactly which one is best. Our time series is small, and our validation set is even smaller. In such cases, it's very easy to overfit the whole forecasting exercise to such a small validation set. That's especially true if the number of available models and their degrees of freedom is high; so always take results with a grain of salt (especially on small datasets), and apply the scientific method before making any kind of forecast!"
   ]
  }
 ],
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